JP2021067258A - Vehicle control device, vehicle control system, and vehicle learning device - Google Patents

Abstract

To provide a vehicle control device that allows reduction in the number of man-hours necessary for an expert in setting a relationship between a state of a vehicle and an action variable.SOLUTION: Until one episode ends (S22, S26), a CPU sets a throttle opening degree command value TA* and a retardation amount aop of ignition timing on the basis of time-series data of an accelerator operation amount PA, operates a throttle valve and an ignition device according to them (S12, S14), and acquires a torque Trq, a torque command value Trq* and an acceleration Gx at that time (S16). If the episode ends, the CPU updates an action value function Q by rewarding according to whether the torque Trq and the acceleration Gx satisfy criteria (S28). The CPU changes how to reward, according to driving preference information of a user.SELECTED DRAWING: Figure 2

Description

The present invention relates to a vehicle control device, a vehicle control system, and a vehicle learning device.

For example, Patent Document 1 below describes a control device that operates a throttle valve as an operation unit of an internal combustion engine mounted on a vehicle based on a value obtained by filtering the operation amount of the accelerator pedal.

Japanese Unexamined Patent Publication No. 2016-6327

By the way, in the above filter, it is necessary to set the operation amount of the throttle valve of the internal combustion engine mounted on the vehicle to an appropriate operation amount according to the operation amount of the accelerator pedal. However, it is necessary to spend a lot of man-hours. As described above, conventionally, a skilled person has spent a lot of man-hours to adapt the operation amount of the electronic device in the vehicle according to the state of the vehicle.

Hereinafter, means for solving the above problems and their actions and effects will be described.
1. 1. The storage device includes an execution device and a storage device, and the storage device stores related regulation data that defines a relationship between a vehicle state and an action variable that is a variable related to the operation of an electronic device in the vehicle. Is based on the detection value of the sensor that detects the state of the vehicle, the acquisition process for acquiring the driving preference information that is information on the driving preference of the user, the detection value acquired by the acquisition process, and the related regulation data. Based on the operation process of operating the electronic device based on the determined value of the action variable and the detection value acquired by the acquisition process, a larger reward is given than when the characteristics of the vehicle satisfy the criteria. The reward calculation process, the state of the vehicle based on the detected value acquired by the acquisition process, the value of the action variable used for operating the electronic device, and the reward corresponding to the operation are predetermined. An update process for updating the relevant regulation data as an input to the update mapping is executed, and the updated mapping increases the expected profit for the reward when the electronic device is operated according to the relevant regulation data. The related regulation data updated as described above is output, and the reward calculation process is different rewards when the driving preference information is different even when the characteristics related to the behavior of the vehicle satisfy the same criteria. It is a control device for a vehicle including a process of giving.

In the above configuration, by calculating the reward associated with the operation of the electronic device, it is possible to grasp what kind of reward can be obtained by the operation. Then, based on the reward, the relationship between the state of the vehicle and the behavior variable can be set to an appropriate relationship in the running of the vehicle by updating the relational regulation data by the update mapping according to the reinforcement learning. Therefore, when setting the relationship between the state of the vehicle and the behavior variable to an appropriate relationship in the running of the vehicle, the man-hours required for the expert can be reduced.

By the way, when the reward is uniquely determined, there is a concern that the relationship between the vehicle state learned by reinforcement learning and the behavior variable does not match the driving preference of the user. Therefore, in the above configuration, by acquiring the driving preference information and giving a reward by the reward calculation process based on the driving preference information, it is possible to update the relational regulation data to be in line with the user's driving preference by reinforcement learning. ..

2. The acquisition process includes a process of acquiring the evaluation of the behavior of the vehicle by the user as the driving preference information, and the reward calculation process includes the process of acquiring the driving preference information showing a low evaluation by the acquisition process, the vehicle. The vehicle control device according to 1 above, which includes a process of giving a reward different from that before the evaluation is obtained even if the characteristics related to the behavior of the above are the same.

In the above configuration, the evaluation by the user is acquired as driving preference information, and when the evaluation result is low, the reward is changed. Then, by executing the operation process using the relational regulation data updated by the subsequent update process, it is possible to improve the evaluation by the user.

3. 3. The driving preference information is the vehicle control device according to 1 or 2 above, which includes historical information on acceleration of the vehicle in the front-rear direction.
Since the history of acceleration in the front-rear direction of the vehicle differs depending on how the user operates the accelerator, the history of the same acceleration reflects the driving preference of the user. In view of this point, in the above configuration, by acquiring the acceleration history as driving preference information, the information can be acquired without the user inputting the driving preference information.

4. The driving preference information is the vehicle control device according to any one of 1 to 3 above, which includes historical information on the amount of accelerator operation.
Since the accelerator operation of the user differs depending on the driving preference of the user, the history information of the accelerator operation amount includes the driving preference information. In view of this point, in the above configuration, by acquiring the history of the accelerator operation amount as the driving preference information, the information can be acquired without the user inputting the driving preference information.

5. The vehicle control device according to any one of 1 to 4 above, wherein the acquisition process includes a process of acquiring the analysis result of the user's face image as the driving preference information.
In the above configuration, by acquiring the analysis result of the user's face image as the driving preference information, the information can be acquired without the user inputting the driving preference information.

6. The state of the vehicle includes a change in the accelerator operation amount, and the reward calculation process has a larger reward than when the acceleration in the front-rear direction of the vehicle due to the change in the accelerator operation amount does not satisfy the reference. The vehicle control device according to any one of 1 to 5 above, which includes a process of giving.

Since the magnitude of the acceleration in the front-rear direction of the vehicle caused by the change in the accelerator operation amount is greatly related to the running performance of the vehicle, a reward is given according to whether or not the acceleration satisfies the standard as in the above configuration. Therefore, it is possible to learn the value of an appropriate action variable by reinforcement learning in order to obtain the desired running performance according to the state of the vehicle.

In particular, in the above configuration, by changing the method of giving rewards according to the driving preference information, it is possible to learn the values of behavior variables appropriate for achieving appropriate driving performance for the driving preference by reinforcement learning.

7. The vehicle includes an internal combustion engine as a thrust generator of the vehicle, the electronic device includes a throttle valve of the internal combustion engine, and the action variable includes a variable related to the opening degree of the throttle valve. The vehicle control device according to the above 6 which is included.

For example, in an internal combustion engine or the like in which the injection amount is adjusted according to the intake air amount, the torque and output of the internal combustion engine greatly change according to the opening degree of the throttle valve. Therefore, the propulsive force of the vehicle can be suitably adjusted by using the variable related to the opening degree of the throttle valve as the action variable for the accelerator operation amount.

8. The execution device and the storage device according to any one of the above 1 to 7 are provided, and the execution device includes a first execution device mounted on the vehicle and a second execution device different from the in-vehicle device. The first executing device executes at least the acquisition process and the operation process, and the second executing device is a vehicle control system that executes at least the update process.

In the above configuration, by executing the update process by the second execution device, the calculation load of the first execution device can be reduced as compared with the case where the update process is executed by the first execution device.
The fact that the second executing device is a device different from the in-vehicle device means that the second executing device is not an in-vehicle device.

9. It is a vehicle control device including the first execution device according to the above 8.
10. It is a learning device for a vehicle including the second executing device according to the above 8.

The figure which shows the control device and the drive system which concerns on 1st Embodiment. The flow chart which shows the procedure of the process executed by the control device which concerns on the same embodiment. The flow chart which shows the detailed procedure of a part of the processing executed by the control device which concerns on this embodiment. The flow chart which shows the procedure of the process executed by the control device which concerns on the same embodiment. The flow chart which shows the procedure of the process executed by the control apparatus which concerns on 2nd Embodiment. The figure which shows the structure of the control system which concerns on 3rd Embodiment. (A) and (b) are flow charts showing the procedure of processing executed by the control system.

<First Embodiment>
Hereinafter, the first embodiment of the vehicle control device will be described with reference to the drawings.
FIG. 1 shows the configuration of the drive system and the control device of the vehicle VC1 according to the present embodiment.

As shown in FIG. 1, the intake passage 12 of the internal combustion engine 10 is provided with a throttle valve 14 and a fuel injection valve 16 in this order from the upstream side, and is injected from the air or fuel injection valve 16 sucked into the intake passage 12. The fuel is discharged into the combustion chamber 24 partitioned by the cylinder 20 and the piston 22 as the intake valve 18 is opened. In the combustion chamber 24, the air-fuel mixture is subjected to combustion with the spark discharge of the ignition device 26, and the energy generated by the combustion is converted into the rotational energy of the crankshaft 28 via the piston 22. To. The air-fuel mixture used for combustion is discharged to the exhaust passage 32 as exhaust gas when the exhaust valve 30 is opened. The exhaust passage 32 is provided with a catalyst 34 as an aftertreatment device for purifying the exhaust gas.

The input shaft 52 of the transmission 50 can be mechanically connected to the crankshaft 28 via a torque converter 40 provided with a lockup clutch 42. The transmission 50 is a device that makes the gear ratio variable, which is the ratio of the rotation speed of the input shaft 52 to the rotation speed of the output shaft 54. A drive wheel 60 is mechanically connected to the output shaft 54.

The control device 70 targets the internal combustion engine 10, and in order to control the torque and the exhaust component ratio, which are the controlled amounts thereof, the control device 70 controls the operation unit of the internal combustion engine 10 such as the throttle valve 14, the fuel injection valve 16, and the ignition device 26. Manipulate. Further, the control device 70 controls the torque converter 40 and operates the lockup clutch 42 in order to control the engaged state of the lockup clutch 42. Further, the control device 70 targets the transmission 50 as a control target, and operates the transmission 50 to control the gear ratio as the control amount thereof. Note that FIG. 1 shows the operation signals MS1 to MS5 of the throttle valve 14, the fuel injection valve 16, the ignition device 26, the lockup clutch 42, and the transmission 50, respectively.

The control device 70 controls the intake air amount Ga detected by the air flow meter 80, the opening degree of the throttle valve 14 (throttle opening degree TA) detected by the throttle sensor 82, and the crank angle sensor 84 for controlling the control amount. Refer to the output signal Scr of. Further, the control device 70 includes the amount of depression of the accelerator pedal 86 (accelerator operation amount PA) detected by the accelerator sensor 88, the acceleration Gx in the front-rear direction of the vehicle VC1 detected by the acceleration sensor 90, and the user's face by the camera 92. Refer to the value of the evaluation variable VV determined by the operation of the image and the evaluation switch 94. Here, the evaluation switch 94 is a human-machine interface for the user of the vehicle VC1 to select one of the three options related to the traveling performance of the vehicle VC1. Here, the three options are "too high", "just right", and "too low" in terms of responsiveness.

The control device 70 includes a CPU 72, a ROM 74, an electrically rewritable non-volatile memory (storage device 76), and a peripheral circuit 78, which can communicate with each other via the local network 79. Here, the peripheral circuit 78 includes a circuit that generates a clock signal that defines the internal operation, a power supply circuit, a reset circuit, and the like.

The control program 74a and the learning program 74b are stored in the ROM 74. On the other hand, the storage device 76 has the relational regulation data DR that defines the relationship between the accelerator operation amount PA, the command value of the throttle opening degree TA (throttle opening degree command value TA *), and the retard angle amount aop of the ignition device 26. It is remembered. Here, the retard angle amount aop is a retard angle amount with respect to a predetermined reference ignition timing, and the reference ignition timing is a timing on the retard side of the MBT ignition timing and the knock limit point. The MBT ignition timing is the ignition timing at which the maximum torque can be obtained (maximum torque ignition timing). The knock limit is an ignition timing advance limit that can keep knocking within an acceptable level under the best possible conditions when using a high octane fuel with a high knock limit. Further, the torque output mapping data DT is stored in the storage device 76. The torque output map defined by the torque output map data DT is a map that outputs the torque Trq by inputting the rotation speed NE, the filling efficiency η, and the ignition timing aig of the crankshaft 28.

FIG. 2 shows a procedure of processing executed by the control device 70 according to the present embodiment. The process shown in FIG. 2 is realized by the CPU 72 repeatedly executing the control program 74a and the learning program 74b stored in the ROM 74, for example, at a predetermined cycle. In the following, the step number of each process is indicated by a number prefixed with "S".

In the series of processes shown in FIG. 2, the CPU 72 first, as the state s, time-series data composed of six sampling values "PA (1), PA (2), ... PA (6)" of the accelerator operation amount PA. (S10). Here, each sampling value constituting the time series data is sampled at different timings from each other. In the present embodiment, time-series data is configured by six sampling values that are adjacent to each other in time series when sampling is performed at a constant sampling cycle.

Next, the CPU 72 sets the action a including the throttle opening degree command value TA * and the retard angle amount aop according to the state s acquired by the process of S10, in accordance with the policy π defined by the relational regulation data DR (S12).

In the present embodiment, the relational regulation data DR is data that defines the action value function Q and the policy π. In the present embodiment, the action value function Q is a table-type function showing the value of the expected return according to the eight-dimensional independent variables of the state s and the action a. Further, the policy π is predetermined while preferentially selecting the action a (greedy action) that is the largest among the action value functions Q in which the independent variable is given the state s when the state s is given. With the probability of, a rule for selecting other actions a is established.

Specifically, the number of values that can be taken by the independent variable of the action value function Q according to the present embodiment is reduced by a part of all combinations of possible values of the state s and the action a by human knowledge or the like. It is a thing. That is, for example, in the time series data of the accelerator operation amount PA, one of the two adjacent sampling values becomes the minimum value of the accelerator operation amount PA and the other becomes the maximum value from the operation of the accelerator pedal 86 by a person. The action value function Q is not defined as it cannot occur. In the present embodiment, the possible values of the state s that defines the action value function Q are limited to 10 4 or less, more preferably 10 3 or less, by dimensionality reduction based on human knowledge or the like.

Next, the CPU 72 outputs the operation signal MS1 to the throttle valve 14 to operate the throttle opening degree TA based on the set throttle opening degree command value TA * and the retard angle amount aop, and operates the throttle opening degree TA, and at the same time, operates the operation signal MS3 to the ignition device 26. Is output to control the ignition timing (S14). Here, in the present embodiment, since the feedback control of the throttle opening degree TA to the throttle opening degree command value TA * is illustrated, even if the throttle opening degree command value TA * is the same value, the operation signal MS1 Can be different signals from each other. Further, for example, when a well-known knocking control (KCS) or the like is performed, the ignition timing is a value obtained by retarding the reference ignition timing with a retard angle amount aop and feedback-corrected by KCS. Here, the reference ignition timing is variably set by the CPU 72 according to the rotation speed NE of the crankshaft 28 and the filling efficiency η. The rotation speed NE is calculated by the CPU 72 based on the output signal Scr of the crank angle sensor 84. Further, the filling efficiency η is calculated by the CPU 72 based on the rotation speed NE and the intake air amount Ga.

Next, the CPU 72 acquires the torque Trq of the internal combustion engine 10, the torque command value Trq * for the internal combustion engine 10, and the acceleration Gx (S16). Here, the CPU 112 calculates the torque Trq by inputting the rotation speed NE, the filling efficiency η, and the ignition timing into the torque output map. Further, the CPU 72 sets the torque command value Trq * according to the accelerator operation amount PA.

Next, the CPU 72 determines whether or not the transient flag F is “1” (S18). When the transient flag F is "1", it indicates that it is in transient operation, and when it is "0", it indicates that it is not in transient operation. When the CPU 72 determines that the transient flag F is "0" (S18: NO), the CPU 72 determines whether or not the absolute value of the change amount ΔPA of the accelerator operation amount PA per unit time is equal to or greater than the predetermined amount ΔPAth. (S20). Here, the change amount ΔPA may be, for example, the difference between the latest accelerator operation amount PA at the execution timing of the process of S20 and the accelerator operation amount PA before the same timing by a unit time.

When the CPU 72 determines that the predetermined amount is ΔPAth or more (S20: YES), the CPU 72 substitutes “1” for the transient flag F (S22).
On the other hand, when the CPU 72 determines that the transient flag F is "1" (S18: YES), the CPU 72 determines whether or not a predetermined period has elapsed from the execution of the process of S22 (S24). Here, the predetermined period is a period until the state in which the absolute value of the change amount ΔPA of the accelerator operation amount PA per unit time is smaller than the predetermined amount ΔPAth and is equal to or less than the specified amount continues for a predetermined time. When the CPU 72 determines that the predetermined period has elapsed (S24: YES), the CPU 72 substitutes “0” for the transient flag F (S26).

When the processing of S22 and S26 is completed, the CPU 72 updates the action value function Q by reinforcement learning, assuming that one episode has ended (S28).
FIG. 3 shows the details of the processing of S28.

In the series of processes shown in FIG. 3, the CPU 72 performs time-series data including a set of three sampling values of the torque command value Trq *, the torque Trq, and the acceleration Gx in the most recently completed episode, and the state s and the action a. (S30). Here, the latest episode is a period in which the transient flag F is continuously set to "0" when the processing of S30 is performed after the processing of S22, and the processing of S26 is followed by the processing of S30. When the processing is performed, it is the period during which the transient flag F is continuously set to "1".

FIG. 3 shows that the values in parentheses that differ are the values of the variables at different sampling timings. For example, the torque command value Trq * (1) and the torque command value Trq * (2) have different sampling timings. Further, the time-series data of the action a belonging to the latest episode is defined as the action set Aj, and the time-series data of the state s belonging to the same episode is defined as the state set Sj.

Next, the CPU 72 has a condition (a) that the absolute value of the difference between the arbitrary torque Trq belonging to the latest episode and the torque command value Trq * is the specified amount ΔTrq or less, and the acceleration Gx is the lower limit value GxL or more. It is determined whether or not the logical product with the condition (a) indicating that the upper limit value is GxH or less is true (S32).

Here, the CPU 72 variably sets the specified amount ΔTrq according to the amount of change ΔPA per unit time of the accelerator operation amount PA at the start of the episode. That is, when the CPU 72 determines that the episode is related to the transient time based on the change amount ΔPA of the accelerator operation amount PA at the start of the episode, the specified amount ΔTrq is set to a larger value than in the steady state. Set to.

Further, the CPU 72 variably sets the lower limit value GxL according to the change amount ΔPA of the accelerator operation amount PA at the start of the episode. That is, when the CPU 72 is an episode related to the transient time and the amount of change ΔPA is positive, the CPU 72 sets the lower limit value GxL to a larger value than the case of the episode related to the steady state. Further, when the CPU 72 is an episode related to the transient time and the amount of change ΔPA is negative, the CPU 72 sets the lower limit value GxL to a smaller value than the case of the episode related to the steady state.

Further, the CPU 72 variably sets the upper limit value GxH according to the change amount ΔPA per unit time of the accelerator operation amount PA at the start of the episode. That is, when the CPU 72 is an episode related to the transient time and the amount of change ΔPA is positive, the CPU 72 sets the upper limit value GxH to a larger value than the case of the episode related to the steady state. Further, when the CPU 72 is an episode related to the transient time and the amount of change ΔPA is negative, the CPU 72 sets the upper limit value GxH to a smaller value than the case of the episode related to the steady state.

When the CPU 72 determines that the logical product is true (S32: YES), it substitutes "10" for the reward r (S34), while when it determines that the logical product is false (S32: NO), the reward r is "". -10 ”is substituted (S36). When the processing of S34 and S36 is completed, the CPU 72 updates the relational regulation data DR stored in the storage device 76 shown in FIG. In this embodiment, the ε soft policy on-type Monte Carlo method is used.

That is, the CPU 72 adds the reward r to the profit R (Sj, Aj) determined by the set of each state read by the process of S30 and the corresponding action (S38). Here, "R (Sj, Aj)" is a general description of the profit R in which one of the elements of the state set Sj is a state and one of the elements of the action set Aj is an action. Next, each of the revenues R (Sj, Aj) determined by the set of each state read by the process of S30 and the corresponding action is averaged and substituted into the corresponding action value function Q (Sj, Aj) ( S40). Here, the averaging may be a process of dividing the profit R calculated by the process of S38 by the value obtained by adding a predetermined number to the number of times the process of S38 is performed. The initial value of the profit R may be the initial value of the corresponding action value function Q.

Next, the CPU 72 describes the throttle opening command value TA * and the retard angle amount aop at the maximum value of the corresponding action value functions Q (Sj, A) for the state read by the process of S30. The set of actions is substituted into the action Aj * (S42). Here, "A" indicates an arbitrary action that can be taken. The action Aj * has a different value depending on the type of the state read by the process of S30, but here, the notation is simplified and described by the same symbol.

Next, the CPU 72 updates the corresponding policy π (Aj | Sj) for each of the states read by the process of S30 (S44). That is, assuming that the total number of actions is "| A |", the selection probability of the action Aj * selected by S42 is "1-ε + ε / | A |". Further, the selection probabilities of "| A | -1" actions other than the action Aj * are set to "ε / | A |", respectively. Since the process of S44 is a process based on the action value function Q updated by the process of S40, the relationship rule data DR that defines the relationship between the state s and the action a increases the profit R. Will be updated to.

When the process of S44 is completed, the CPU 72 temporarily ends the series of processes shown in FIG.
Returning to FIG. 2, the CPU 72 temporarily ends the series of processes shown in FIG. 2 when the process of S28 is completed or when a negative determination is made in the processes of S20 and S24. The processing of S10 to S26 is realized by the CPU 72 executing the control program 74a, and the processing of S28 is realized by the CPU 72 executing the learning program 74b. Further, the relational regulation data DR at the time of shipment of the vehicle VC1 is data that has been learned in advance by executing the same processing as the processing shown in FIG. 2 while simulating the running of the vehicle on the test bench.

FIG. 4 shows a procedure of the process of changing the reference in the process of S32. The process shown in FIG. 4 is realized by the CPU 72 repeatedly executing the learning program 74b stored in the ROM 74, for example, at a predetermined cycle.

In the series of processes shown in FIG. 4, the CPU 72 first determines whether or not there is an evaluation input by operating the evaluation switch 94 (S50). Then, when the CPU 72 determines that there is an evaluation input (S50: YES), the CPU 72 determines whether or not the evaluation input is an input indicating that the responsiveness is too low (S52). Then, when the CPU 72 determines that the input is "too low responsiveness" (S52: YES), the CPU 72 reduces the specified amount ΔTrq at the time of transition, and the upper limit value GxH when the change amount ΔPA is positive. And the lower limit value GxL is increased, and the upper limit value GxH and the lower limit value GxL when the change amount ΔPA is negative are decreased (S54).

On the other hand, when a negative determination is made in the process of S52, the CPU 72 determines whether or not the evaluation input is an input indicating that the response is too high (S56). Then, when the CPU 72 determines that the input is "too responsive" (S56: YES), the CPU 72 expands the specified amount ΔTrq at the time of transition, and the upper limit value GxH when the change amount ΔPA is positive. And the lower limit value GxL is decreased, and the upper limit value GxH and the lower limit value GxL when the change amount ΔPA is negative are increased (S58).

The CPU 72 temporarily ends a series of processes shown in FIG. 4 when the processes of S54 and S58 are completed or when a negative determination is made in the processes of S50 and S56.
Here, the operation and effect of this embodiment will be described.

The CPU 72 acquires time-series data of the accelerator operation amount PA in accordance with the operation of the accelerator pedal 86 by the user, and sets the action a including the throttle opening degree command value TA * and the retard angle amount aop according to the policy π. Here, the CPU 72 basically selects the action a that maximizes the expected return based on the action value function Q defined in the relational rule data DR. However, the CPU 72 searches for the action a that maximizes the expected return by selecting an action other than the action a that maximizes the expected return at a predetermined ratio ε. As a result, the related regulation data DR can be updated by reinforcement learning as the user drives the vehicle VC1. As a result, the throttle opening command value TA * and the retard angle amount aop according to the accelerator operation amount PA can be set to appropriate values in the traveling of the vehicle VC1 without excessively increasing the man-hours by the expert.

In particular, in the present embodiment, the running performance of the vehicle can be evaluated by the user operating the evaluation switch 94. Then, the standard regarding the absolute value of the difference between the torque Trq and the torque command value Trq * and the standard regarding the acceleration Gx in giving the reward r are changed according to the evaluation result by the user. As a result, the reference for the absolute value of the difference between the torque Trq and the torque command value Trq * and the reference for the acceleration Gx can be made appropriate for the driving preference of the user. Therefore, as the reinforcement learning progresses with the driving of the user, the relational regulation data DR can be updated to the data appropriate for the driving preference of the user.

According to the present embodiment described above, the effects described below can be further obtained.
(1) Time-series data of the accelerator operation amount PA was included in the independent variable of the action value function Q. As a result, the value of the action a can be finely adjusted for various changes in the accelerator operation amount PA, as compared with the case where only a single sampling value is used as the independent variable for the accelerator operation amount PA.

(2) The throttle opening command value TA * itself is included in the independent variable of the action value function Q. This makes it easier to increase the degree of freedom of search by reinforcement learning, for example, as compared with the case where the parameters of the model formula that models the behavior of the throttle opening command value TA * are used as independent variables related to the throttle opening. Is.

<Second embodiment>
Hereinafter, the second embodiment will be described with reference to the drawings, focusing on the differences from the first embodiment.

FIG. 5 shows a procedure of the process for changing the reference in the process of S32 according to the present embodiment. The process shown in FIG. 5 is realized by the CPU 72 repeatedly executing the learning program 74b stored in the ROM 74, for example, at a predetermined cycle. In FIG. 5, the same step numbers are assigned to the processes corresponding to the processes shown in FIG. 4 for convenience.

In the series of processes shown in FIG. 5, the CPU 72 first acquires the accelerator operation amount PA and the acceleration Gx (S60). Next, the CPU 72 determines whether or not a predetermined period has elapsed after the change amount ΔPA of the accelerator operation amount PA per unit time becomes a predetermined amount ΔPAth or more (S62). Here, the predetermined period is a period from when the change amount ΔPA of the accelerator operation amount PA per unit time becomes small until the predetermined time elapses.

When the CPU 72 determines that the predetermined period has elapsed (S62: YES), the CPU 72 acquires the face image data (S64). Then, the CPU 72 analyzes the face image data to determine whether or not the user is uncomfortable with the driving performance, and stores the result in the storage device 76 (S66). When the processing of S66 is completed or when a negative determination is made in the processing of S62, the CPU 72 determines whether or not it is the end of the trip (S68). Here, the trip is a period of one time in which the traveling permission signal of the vehicle is in the ON state. In the present embodiment, the travel permission signal corresponds to an ignition signal.

When the CPU 72 determines that it is the end of the trip (S68: YES), the CPU 72 reads out the time-series data of the accelerator operation amount PA and the acceleration Gx acquired by the processing of S60 in the trip (S70). Then, the CPU 72 determines whether or not the logical product of the following conditions (f) to (c) is true (S72).

Condition (f): It is a condition that the maximum value of the accelerator operation amount PA is equal to or more than the specified value PAH. Here, the specified value PAH is set to a value larger than the assumed maximum value of the accelerator operation amount PA generated by the operation of the accelerator pedal 86 by a general user.

Condition (g): It is a condition that the maximum value of the acceleration Gx of the vehicle VC1 is equal to or higher than the specified value GxHH. Here, the specified value GxHH is set to a value larger than the assumed maximum value of the acceleration Gx generated by the operation of the accelerator pedal 86 by a general user.

Condition (c): It is a condition that the analysis result of the face image data by the processing of S66 shows discomfort in the driving performance.
When the CPU 72 determines that the logical product of the condition (f) to the condition (c) is true (S72: YES), the CPU 72 executes the process of S54. That is, if the logical product of the condition (f) and the condition (g) is true, there is a possibility that the user is trying to accelerate the vehicle VC1 by pressing the accelerator pedal 86 strongly, and the condition (c) is satisfied. If this is the case, there is a possibility that the accelerator pedal 86 is depressed more strongly than a general user because he / she is dissatisfied with the responsiveness of the vehicle VC1. Therefore, the conditions for giving the reward are changed so that the acceleration performance of the vehicle VC1 can be improved.

On the other hand, when the CPU 72 determines that the logical product of the condition (f) to the condition (c) is false (S72: NO), the following conditions (sa), condition (s) and condition (c) It is determined whether or not the logical product is true (S74).

Condition (s): It is a condition that the maximum value of the accelerator operation amount PA is equal to or less than the specified value PAL. Here, the specified value PAL is set to a value smaller than the assumed maximum value of the accelerator operation amount PA generated by the operation of the accelerator pedal 86 by a general user.

Condition (s): It is a condition that the maximum value of the acceleration Gx of the vehicle VC1 is equal to or less than the specified value GxLL. Here, the specified value GxLL is set to a value smaller than the assumed maximum value of the acceleration Gx generated by the operation of the accelerator pedal 86 by a general user.

When the CPU 72 determines that the logical product of the condition (s), the condition (s), and the condition (c) is true (S74: YES), the CPU 72 executes the process of S58. That is, when the logical product of the condition (sa) and the condition (shi) is true, the user of the vehicle VC1 tends to step on the accelerator pedal 86 lightly as compared with a general user, and the condition (c) is satisfied. If so, there is a possibility that the acceleration applied to the vehicle VC1 is too large and the vehicle feels uncomfortable. Therefore, the condition for giving the reward is changed so that the acceleration perceived by the user when the vehicle VC1 is accelerated can be made smaller.

The CPU 72 temporarily ends a series of processes shown in FIG. 5 when the processes of S54 and S58 are completed or when a negative determination is made in the processes of S68 and S74.
As described above, in the present embodiment, the condition for acquiring the driving preference information from the driving information of the vehicle VC1 by the user and giving a reward according to the information is obtained without the user performing the operation of inputting the evaluation of the driving performance. Can be changed.

<Third embodiment>
Hereinafter, the third embodiment will be described with reference to the drawings, focusing on the differences from the first embodiment.

In the present embodiment, the update of the related regulation data DR is executed outside the vehicle VC1.
FIG. 6 shows the configuration of the control system that executes reinforcement learning in the present embodiment. In FIG. 6, the members corresponding to the members shown in FIG. 1 are designated by the same reference numerals for convenience.

The ROM 74 in the control device 70 in the vehicle VC1 shown in FIG. 6 stores the control program 74a, but does not store the learning program 74b. Further, the control device 70 includes a communication device 77. The communication device 77 is a device for communicating with the data analysis center 110 via the network 100 outside the vehicle VC1.

The data analysis center 110 analyzes the data transmitted from the plurality of vehicles VC1, VC2, .... The data analysis center 110 includes a CPU 112, a ROM 114, an electrically rewritable non-volatile memory (storage device 116), peripheral circuits 118, and a communication device 117, which are made communicable by the local network 119. Is. The learning program 74b is stored in the ROM 114, and the related regulation data DR is stored in the storage device 116.

FIG. 7 shows a processing procedure of reinforcement learning according to the present embodiment. The process shown in FIG. 7A is realized by the CPU 72 executing the control program 74a stored in the ROM 74 shown in FIG. Further, the process shown in FIG. 7B is realized by the CPU 112 executing the learning program 74b stored in the ROM 114. Note that the processes corresponding to the processes shown in FIGS. 3 and 4 in FIG. 7 are assigned the same step numbers for convenience. In the following, the process shown in FIG. 7 will be described along with the time series of reinforcement learning.

In the series of processes shown in FIG. 7A, the CPU 72 executes the processes of S10 to S26. Then, when the CPU 72 completes the processes of S22 and S26, the CPU 72 transmits the data necessary for the learning process by operating the communication device 77 (S80). Here, the data to be transmitted includes the time series data of the torque command value Trq *, the torque Trq and the acceleration Gx in the episode ended immediately before the execution of the processes of S22 and S26, and the state set Sj and the action set Aj. .. Further, the CPU 72 determines whether or not there is an evaluation input by operating the evaluation switch 94 (S82), and if it determines that there is an input (S82: YES), operates the communication device 77 to obtain data on the evaluation result. Is transmitted (S84).

On the other hand, as shown in FIG. 7B, the CPU 112 receives the data transmitted by the processing of S80 (S100), and determines whether or not there is transmission of the evaluation result data by the processing of S84 (S). S102). Then, when it is determined that the evaluation result data is transmitted (S102: YES), the CPU 112 receives the evaluation result (S104) and executes the processes S52 to S58.

When the CPU 112 completes the processing of S54 and S58, or when a negative determination is made in the processing of S56 and S102, the CPU 112 updates the relational regulation data DR based on the data received by the processing of S100 (S28). Then, the CPU 112 determines whether or not the number of updates of the related specified data DR is equal to or greater than a predetermined number of times (S106), and if it determines that the number of updates is equal to or greater than the predetermined number of times (S106: YES), operates the communication device 117. The relevant regulation data DR is transmitted to the vehicle VC1 that has transmitted the data received by the processing of S100 (S108). The CPU 112 temporarily ends a series of processes shown in FIG. 7B when the process of S108 is completed or when a negative determination is made in the process of S106.

On the other hand, as shown in FIG. 7A, the CPU 72 determines whether or not there is updated data (S86), and when it is determined that there is updated data (S86: YES), the updated relational regulation data DR is displayed. Receive (S88). Then, the CPU rewrites the relational regulation data DR stored in the storage device 76 with the received relational regulation data DR (S90). The CPU 72 temporarily ends a series of processes shown in FIG. 7A when the process of S90 is completed or when a negative determination is made in the processes of S20, S24, and S86.

As described above, according to the present embodiment, since the update processing of the related regulation data DR is performed outside the vehicle VC1, the calculation load of the control device 70 can be reduced.
<Correspondence>
The correspondence between the matters in the above-described embodiment and the matters described in the above-mentioned "means for solving the problem" column is as follows. In the following, the correspondence is shown for each number of the solution means described in the column of "Means for solving the problem".

[1] The execution device corresponds to the CPU 72 and the ROM 74, and the storage device corresponds to the storage device 76. The acquisition process corresponds to the processes of S10, S16, S50, S60, S66, the operation process corresponds to the process of S14, the reward calculation process corresponds to the processes of S32 to S36, and the update process corresponds to the processes of S38 to S38. Corresponds to the processing of S44. [2] Corresponds to the process of FIG. [3-5] Corresponds to the process of FIG. [6] The reference of acceleration corresponds to the range defined by the lower limit value GxL and the upper limit value GxH. [7] The variable related to the throttle valve opening degree corresponds to the throttle opening degree command value TA *. [8-10] The first executor corresponds to the CPU 72 and ROM 74, and the second executor corresponds to the CPU 112 and ROM 114.

<Other Embodiments>
In addition, this embodiment can be implemented by changing as follows. The present embodiment and the following modified examples can be implemented in combination with each other within a technically consistent range.

・ "About driving preference information"
In the above embodiment, the evaluation of the behavior of the vehicle by the user is obtained by operating the evaluation switch 94, but the present invention is not limited to this. For example, the vehicle VC1 may be provided with a microphone, and when the user says "slow" at the time of acceleration or the like, information indicating that the feeling of acceleration is low may be acquired.

In FIG. 5, as driving preference information, the maximum value of the accelerator operation amount PA, the maximum value of the acceleration Gx, and the analysis result of the face image data are used, but the present invention is not limited to this. For those three pieces of information, only two or only one of them may be used. Moreover, you may use the minimum value of acceleration Gx. As a result, the magnitude of the absolute value of the acceleration Gx during deceleration can be used as driving preference information.

・ "About behavior variables"
In the above embodiment, the throttle opening command value TA * is exemplified as a variable related to the opening degree of the throttle valve as an action variable, but the present invention is not limited to this. For example, the responsiveness of the throttle opening command value TA * to the accelerator operation amount PA is expressed by the wasted time and the second-order lag filter, and the wasted time and the two variables that define the second-order lag filter are a total of three. The variable may be a variable related to the opening degree of the throttle valve. However, in that case, it is desirable that the state variable is a change amount per unit time of the accelerator operation amount PA instead of the time series data of the accelerator operation amount PA.

In the above embodiment, the retard angle amount aop is exemplified as a variable related to the ignition timing as an action variable, but the present invention is not limited to this. For example, it may be the ignition timing itself to be corrected by KCS.

In the above embodiment, variables related to the opening degree of the throttle valve and variables related to the ignition timing are exemplified as behavioral variables, but the present invention is not limited to this. For example, the fuel injection amount may be used in addition to the variables related to the opening degree of the throttle valve and the variables related to the ignition timing. Further, regarding these three, only the variable related to the opening degree of the throttle valve and the fuel injection amount may be adopted as the action variables, or only the variable related to the ignition timing and the fuel injection amount may be adopted. Furthermore, for those three, only one of them may be adopted as the behavioral variable.

Further, as described in the column of "About the internal combustion engine", in the case of a compression ignition type internal combustion engine, a variable related to the injection amount is used instead of the variable related to the opening degree of the throttle valve, and a variable related to the ignition timing is used instead of the variable related to the ignition timing. You can use the variables related to. In addition to the variables related to the injection timing, the variables related to the number of injections in one combustion cycle and the end timing of one of the two fuel injections adjacent in time series for one cylinder in one combustion cycle and the other It is desirable to add a variable for the time interval between the start timing.

Further, for example, when the transmission 50 is a stepped transmission, the current value of the solenoid valve for adjusting the engaged state of the clutch by flood control may be used as an action variable.
For example, when a hybrid vehicle, an electric vehicle, or a fuel cell vehicle is adopted as the vehicle as described in the column of "About the vehicle" below, the torque and output of the rotary electric machine may be used as action variables. Further, for example, when an in-vehicle air conditioner including a compressor that is rotated by the rotational power of the crankshaft of the internal combustion engine is provided, the load torque of the compressor may be included in the action variable. Further, when an electric in-vehicle air conditioner is provided, the power consumption of the air conditioner may be included in the action variable.

・ "About the condition"
In the above embodiment, the time-series data of the accelerator operation amount PA is set to the data consisting of six values sampled at equal intervals, but the present invention is not limited to this. The data may be data consisting of two or more sampling values at different sampling timings, and at this time, it is more desirable that the data consists of three or more sampling values or the sampling intervals are evenly spaced.

The state variable related to the accelerator operation amount is not limited to the time series data of the accelerator operation amount PA, but is, for example, the amount of change of the accelerator operation amount PA per unit time as described in the column of "behavior variable". May be good.

For example, as described in the column of "About the action variable", when the current value of the solenoid valve is used as the action variable, the state is adjusted by the rotation speed of the input shaft 52 of the transmission, the rotation speed of the output shaft 54, and the solenoid valve. The hydraulic pressure to be applied may be included. Further, for example, as described in the column of "behavior variable", when the torque or output of the rotary electric machine is used as the action variable, the charge rate or temperature of the battery may be included in the state. Further, for example, as described in the column of "behavior variables", when the load torque of the compressor and the power consumption of the air conditioner are included in the behavior, the temperature inside the vehicle interior may be included in the state.

・ "About dimensionality reduction of table format data"
The method for reducing the dimension of the table format data is not limited to the one illustrated in the above embodiment. For example, since it is rare that the accelerator operation amount PA becomes the maximum value, the action value function Q is not defined for the state where the accelerator operation amount PA becomes the specified amount or more, and the accelerator operation amount PA becomes the specified amount or more. In this case, the throttle opening command value TA * and the like may be adapted separately. Further, for example, the dimension may be reduced by excluding the value at which the throttle opening degree command value TA * is equal to or more than the specified value from the values that can be taken.

However, dimensionality reduction is not essential. For example, in the third embodiment, if reinforcement learning based on data from a plurality of vehicles is performed and the computing power of the CPU 72 and the storage capacity of the storage device 76 are sufficient, the dimension reduction is performed before the vehicle is shipped. Although the action value function is learned only for a part of the actions, all actions may be executed by exploration after shipment. As a result, it is possible to increase the number of actions that can be taken as a search and find more appropriate actions in view of the fact that sufficient learning data can be secured after shipping as compared with before shipping.

・ "Regarding related regulation data"
In the above embodiment, the action value function Q is a table-type function, but the present invention is not limited to this. For example, a function approximator may be used.

For example, instead of using the action value function Q, the policy π is expressed by a function approximation device in which the state s and the action a are independent variables and the probability of taking the action a is the dependent variable, and the parameter that determines the function approximation device is expressed. , May be updated according to the reward r.

・ "About operation processing"
For example, as described in the column of "Relationship regulation data", when the action value function is used as a function approximation device, all the discrete value sets for the action that are the independent variables of the function of the table type in the above embodiment. The action a that maximizes the action value function Q may be selected by inputting the action value function Q together with the state s.

Further, for example, as described in the column of "Relationship regulation data", when the policy π is a function approximation device in which the state s and the action a are independent variables and the probability of taking the action a is the dependent variable, the policy π is used. Action a may be selected based on the probability indicated by.

・ "About updated mapping"
In the processing of S38 to S44, the method by the ε soft policy on-type Monte Carlo method was illustrated, but the present invention is not limited to this. For example, the policy-off type Monte Carlo method may be used. However, it is not limited to the Monte Carlo method, for example, the policy-off type TD method is used, the policy-on type TD method such as the SARSA method is used, and the eligibility tracing method is used as the policy-on type learning, for example. You may use it.

Further, for example, as described in the column of "Relationship regulation data", when the policy π is expressed by using a function approximation device and this is directly updated based on the reward r, the policy gradient method or the like is used to update the policy π. A map may be constructed.

Further, only one of the action value function Q and the policy π is not limited to the one directly updated by the reward r. For example, the action value function Q and the policy π may be updated, respectively, as in the actor-critic method. Further, in the actor-critic method, the value function V may be updated instead of the action value function Q, for example.

The “ε” that defines the policy π is not limited to a fixed value, and may be changed according to a predetermined rule according to the degree of learning progress.
・ "About reward calculation processing"
In the process of FIG. 3, the reward is given according to whether or not the logical product of the condition (a) and the condition (b) is true, but the reward is not limited to this. For example, a process of giving a reward depending on whether or not the condition (a) is satisfied and a process of giving a reward depending on whether or not the condition (b) is satisfied may be executed. Further, for example, with respect to two processes, that is, a process of giving a reward depending on whether or not the condition (a) is satisfied and a process of giving a reward depending on whether or not the condition (b) is satisfied, among them. Only one of the processes may be executed.

Further, for example, instead of giving the same reward uniformly when the condition (a) is satisfied, a process may be performed in which a larger reward is given when the absolute value of the difference between the torque Trq and the torque command value Trq * is smaller than when it is large. .. Further, for example, instead of giving the same reward uniformly when the condition (a) is not satisfied, it is also possible to give a smaller reward when the absolute value of the difference between the torque Trq and the torque command value Trq * is large than when it is small. Good.

Further, for example, instead of giving the same reward uniformly when the condition (a) is satisfied, the process may be such that the magnitude of the reward is variable according to the magnitude of the acceleration Gx. Further, for example, instead of giving the same reward uniformly when the condition (a) is not satisfied, the process may be such that the magnitude of the reward is variable according to the magnitude of the acceleration Gx.

Further, for example, when the current value of the solenoid valve of the transmission 50 is used as the action variable as described in the column of "about the action variable", for example, the following three processes (a) to (c) are performed in the reward calculation process. At least one of these processes may be included.

(A) This is a process in which when the time required for switching the gear ratio by the transmission is within a predetermined time, a larger reward is given than when the predetermined time is exceeded.
(B) This is a process in which when the absolute value of the change speed of the rotation speed of the input shaft 52 of the transmission is equal to or less than the input side predetermined value, a larger reward is given than when the input side predetermined value is exceeded.

(C) This is a process in which when the absolute value of the change speed of the rotation speed of the output shaft 54 of the transmission is equal to or less than the predetermined value on the output side, a larger reward is given than when the predetermined value on the output side is exceeded.
In such a case, for example, when an evaluation indicating that the responsiveness is too low is input by operating the evaluation switch 94, the predetermined time is set shorter, while the predetermined value on the input side and the predetermined value on the output side are larger. You can set it to a value.

In addition, for example, as described in the column of "About behavior variables", when the torque or output of a rotating electric machine is used as a behavior variable, a process of giving a larger reward than when the battery charge rate is within a predetermined range. Alternatively, it may include a process that rewards a greater amount than if the battery temperature is within a predetermined range. In this case, the above condition (a) or the like may be changed according to the driving preference information, but the predetermined range may be changed according to the driving preference information in order to make it easier to satisfy the condition (a) or the like during transient operation. Good.

In addition, for example, as described in the column of "About behavior variables", when the load torque of the compressor and the power consumption of the air conditioner are included in the behavior variables, it is larger than when the temperature inside the vehicle is not within the predetermined range. A rewarding process may be added. In this case, the above condition (a) or the like may be changed according to the driving preference information, but the predetermined range may be changed according to the driving preference information in order to make it easier to satisfy the condition (a) or the like during transient operation. Good.

・ "About acquisition process"
In the above embodiment, the evaluation result of the running performance by the user is acquired by acquiring the evaluation variable VV based on the output signal of the evaluation switch 94, but the present invention is not limited to this. For example, instead of the evaluation switch 94, a device for detecting a voice instruction may be provided, and the detection result may be acquired as an evaluation variable VV.

・ "About vehicle control system"
In the example shown in FIG. 7, the process of determining the action based on the policy π (the process of S12) is executed on the vehicle side, but the present invention is not limited to this. For example, the data acquired by the processing of the vehicle VC1 to S10 may be transmitted, the action a may be determined using the data transmitted at the data analysis center 110, and the determined action may be transmitted to the vehicle VC1.

The vehicle control system is not limited to the one composed of the control device 70 and the data analysis center 110. For example, the user's mobile terminal may be used instead of the data analysis center 110. Further, the vehicle control system may be configured by the control device 70, the data analysis center 110, and the mobile terminal. This can be realized, for example, by executing the process of S12 by a mobile terminal.

・ "About the execution device"
The execution device is not limited to the one provided with the CPU 72 (112) and the ROM 74 (114) to execute software processing. For example, a dedicated hardware circuit such as an ASIC that performs hardware processing on at least a part of what has been software-processed in the above embodiment may be provided. That is, the executing device may have any of the following configurations (a) to (c). (A) A processing device that executes all of the above processing according to a program and a program storage device such as a ROM that stores the program are provided. (B) A processing device and a program storage device that execute a part of the above processing according to a program, and a dedicated hardware circuit that executes the remaining processing are provided. (C) A dedicated hardware circuit for executing all of the above processes is provided. Here, there may be a plurality of software execution devices including a processing device and a program storage device, and a plurality of dedicated hardware circuits.

・ "About storage device"
In the above embodiment, the storage device that stores the related regulation data DR and the storage device (ROM74, 114) that stores the learning program 74b and the control program 74a are different storage devices, but the storage device is not limited to this.

・ "About internal combustion engine"
The internal combustion engine is not limited to one having a port injection valve for injecting fuel into the intake passage 12 as a fuel injection valve, and may be provided with an in-cylinder injection valve for directly injecting fuel into the combustion chamber 24. For example, it may include both a port injection valve and an in-cylinder injection valve.

The internal combustion engine is not limited to the spark ignition type internal combustion engine, and may be, for example, a compression ignition type internal combustion engine that uses light oil or the like as fuel.
・ "About the vehicle"
The vehicle is not limited to a vehicle in which the thrust generator is only an internal combustion engine, and may be, for example, a so-called hybrid vehicle including an internal combustion engine and a rotary electric machine. Further, for example, as the thrust generator, there may be a so-called electric vehicle or a fuel cell vehicle equipped with a rotating electric machine without providing an internal combustion engine.

10 ... Internal combustion engine, 12 ... Intake passage, 14 ... Throttle valve, 16 ... Fuel injection valve, 18 ... Intake valve, 20 ... Cylinder, 22 ... Piston, 24 ... Combustion chamber, 26 ... Ignition device, 28 ... Crankshaft, 30 ... exhaust valve, 32 ... exhaust passage, 34 ... catalyst, 40 ... torque converter, 42 ... lockup clutch, 50 ... transmission, 52 ... input shaft, 54 ... output shaft, 60 ... drive wheel, 70 ... control device, 72 ... CPU, 74 ... ROM, 74a ... Control program, 74b ... Learning program, 76 ... Storage device, 77 ... Communication device, 78 ... Peripheral circuit, 79 ... Local network, 80 ... Airflow meter, 82 ... Throttle sensor, 84 ... Crank Angle sensor, 86 ... Accelerator pedal, 88 ... Accelerator sensor, 90 ... Acceleration sensor, 92 ... Camera, 94 ... Evaluation switch, 100 ... Network, 110 ... Data analysis center, 112 ... CPU, 114 ... ROM, 114a ... Learning program, 116 ... storage device, 117 ... communication device, 118 ... peripheral circuit, 119 ... local network.

Claims (10)

Equipped with an execution device and a storage device
The storage device stores relationship regulation data that defines the relationship between the state of the vehicle and the behavior variable, which is a variable related to the operation of the electronic device in the vehicle.
The executing device is
An acquisition process for acquiring the detection value of the sensor that detects the state of the vehicle and the driving preference information that is information on the driving preference of the user.
An operation process for operating the electronic device based on the value of the action variable determined by the detection value acquired by the acquisition process and the relational regulation data, and
Based on the detected value acquired by the acquisition process, a reward calculation process that gives a larger reward than when the characteristics related to the behavior of the vehicle meet the criteria and does not meet the criteria.
Input of the vehicle state based on the detected value acquired by the acquisition process, the value of the action variable used for the operation of the electronic device, and the reward corresponding to the operation into a predetermined update map. And the update process to update the related regulation data,
And
The updated map outputs the relevant regulation data updated so as to increase the expected profit for the reward when the electronic device is operated according to the relevant regulation data.
The reward calculation process is a vehicle control device including a process of giving different rewards when the driving preference information is different even when the characteristics related to the behavior of the vehicle satisfy the same criteria. The acquisition process includes a process of acquiring the evaluation of the behavior of the vehicle by the user as the driving preference information.
The reward calculation process includes a process of giving a reward different from that before the acquisition of the evaluation even if the characteristics related to the behavior of the vehicle are the same when the driving preference information showing a low evaluation is acquired by the acquisition process. The vehicle control device according to claim 1. The vehicle control device according to claim 1 or 2, wherein the driving preference information includes history information of acceleration in the front-rear direction of the vehicle. The vehicle control device according to any one of claims 1 to 3, wherein the driving preference information includes history information of an accelerator operation amount. The vehicle control device according to any one of claims 1 to 4, wherein the acquisition process includes a process of acquiring the analysis result of the user's face image as the driving preference information. The state of the vehicle includes a change in the amount of accelerator operation.
The reward calculation process according to any one of claims 1 to 5, which includes a process of giving a larger reward than when the acceleration in the front-rear direction of the vehicle due to the change in the accelerator operation amount satisfies the reference. The vehicle control device described. The vehicle is provided with an internal combustion engine as a thrust generator for the vehicle.
The electronic device includes a throttle valve of the internal combustion engine.
The vehicle control device according to claim 6, wherein the action variable includes a variable related to the opening degree of the throttle valve. The executing device and the storage device according to any one of claims 1 to 7 are provided.
The execution device includes a first execution device mounted on the vehicle and a second execution device different from the in-vehicle device.
The first execution device executes at least the acquisition process and the operation process,
The second execution device is a vehicle control system that executes at least the update process. A vehicle control device including the first execution device according to claim 8. A vehicle learning device including the second execution device according to claim 8.

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