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self learning test

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Ciakon
2024-04-22 20:09:47 +02:00
parent f6993a8a92
commit af1cf75b8d
23 changed files with 162800 additions and 273 deletions
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Assets/ML-Agents/Timers/AI training_timers.json
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Assets/Scenes/AI Training.unity
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Assets/Scripts/AgentControllerV7.cs Normal file
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using System.Collections;
using System.Collections.Generic;
using UnityEngine;
using Unity.MLAgents;
using Unity.MLAgents.Sensors;
using Unity.MLAgents.Actuators;
using UnityEngine.UIElements;
using System.Linq;
using Unity.Mathematics;
using Unity.VisualScripting;
using System.Reflection;
using System;
public class AgentControllerV7 : Agent
{
public float motorTorque = 300;
public float brakeTorque = 500;
public float maxSpeed = 400;
public float steeringRange = 9;
public float steeringRangeAtMaxSpeed = 7;
public float autoBrake = 100;
WheelControl[] wheels;
Rigidbody rigidBody;
public List<GameObject> checkpoints;
Vector3 startPosition;
Quaternion startRotation;
int currentStep = 0;
float totalReward = 0;
float totalMentalPain = 0;
int stepsSinceCheckpoint = 0;
int checkpointsReached = 0;
public int maxStepsPerCheckpoint = 300;
public int distanceBetweenCheckpoints = 5;
public bool ignoreMentalPain = true;
// Start is called before the first frame update
void Start()
{
rigidBody = GetComponent<Rigidbody>();
// Find all child GameObjects that have the WheelControl script attached
wheels = GetComponentsInChildren<WheelControl>();
startPosition = transform.localPosition;
startRotation = transform.localRotation;
}
public override void OnEpisodeBegin()
{
stepsSinceCheckpoint = 0;
checkpointsReached = 0;
totalReward = 0;
totalMentalPain = 0;
// reset wheels
foreach (var wheel in wheels)
{
wheel.WheelCollider.brakeTorque = 0;
wheel.WheelCollider.motorTorque = 0;
wheel.WheelCollider.steerAngle = 0;
}
// reset car
transform.localPosition = startPosition;
transform.localRotation = startRotation;
rigidBody.velocity = Vector3.zero;
rigidBody.angularVelocity = Vector3.zero;
// reset checkpoints
foreach (GameObject checkpoint in checkpoints)
{
checkpoint.GetComponent<Checkpoint>().isCollected = false;
}
}
public override void CollectObservations(VectorSensor sensor)
{
Transform currentCheckpoint = checkpoints[0].transform;
foreach (GameObject checkpoint in checkpoints)
{
bool isCollected = checkpoint.GetComponent<Checkpoint>().isCollected;
if (!isCollected)
{
currentCheckpoint = checkpoint.transform;
break;
}
}
// distance to next checkpoint
sensor.AddObservation(distanceToCheckpoint(currentCheckpoint));
// relative angle to checkpoint
sensor.AddObservation(angleToCheckpoint(currentCheckpoint));
// relative vector pointing to checkpoint
Vector3 position = transform.localPosition;
Vector3 checkpointPosition = currentCheckpoint.localPosition;
Vector3 toCheckpoint = new Vector3(
checkpointPosition.x - position.x,
0,
checkpointPosition.z - position.z
);
float carAngle = transform.localEulerAngles.y;
toCheckpoint = Quaternion.Euler(0, -carAngle, 0) * toCheckpoint.normalized;
sensor.AddObservation(toCheckpoint.x);
sensor.AddObservation(toCheckpoint.z);
// relative Velocity
Vector3 velocity = new Vector3(
rigidBody.velocity.x,
0,
rigidBody.velocity.z
);
Vector3 relativeVelocity = Quaternion.Euler(0, -carAngle, 0) * velocity;
sensor.AddObservation(relativeVelocity.x);
sensor.AddObservation(relativeVelocity.z);
}
public override void OnActionReceived(ActionBuffers actions)
{
// Actions size = 2 [vertical speed, horizontal speed] = [-1..1, -1..1] // discrete = [{0, 1, 2}, {0, 1, 2}] = [{-1, 0, 1}...]
float vInput = 0;
float hInput = 0;
if (actions.DiscreteActions[0] == 0)
vInput = -1f;
if (actions.DiscreteActions[0] == 1)
vInput = 1f;
if (actions.DiscreteActions[1] == 0)
hInput = -1f;
if (actions.DiscreteActions[1] == 1)
hInput = 1f;
// give benson mental pain for existing (punishment for maximizing first checkpoint by standing still)
// AddReward(-0.002f);
AddReward(-0.0018f); // less pain because of V4
totalMentalPain -= 0.0018f;
if (ignoreMentalPain)
totalReward -= 0.0018f;
float forwardSpeed = Vector3.Dot(transform.forward, rigidBody.velocity);
// Calculate how close the car is to top speed
// as a number from zero to one
float speedFactor = Mathf.InverseLerp(0, maxSpeed / 4, forwardSpeed);
// Use that to calculate how much torque is available
// (zero torque at top speed)
float currentMotorTorque = Mathf.Lerp(motorTorque, 0, speedFactor);
// …and to calculate how much to steer
// (the car steers more gently at top speed)
float currentSteerRange = Mathf.Lerp(steeringRange, steeringRangeAtMaxSpeed, speedFactor);
// Check whether the user input is in the same direction
// as the car's velocity
bool isAccelerating = Mathf.Sign(vInput) == Mathf.Sign(forwardSpeed);
bool isStopping = vInput == 0; // range
bool isBraking = (vInput < 0 && forwardSpeed > 0) || (vInput > 0 && forwardSpeed < 0);
if (vInput > 0 && forwardSpeed < 0)
{
isAccelerating = false;
}
foreach (var wheel in wheels)
{
// Apply steering to Wheel colliders that have "Steerable" enabled
if (wheel.steerable)
{
wheel.WheelCollider.steerAngle = hInput * currentSteerRange;
}
if (isBraking)
{
wheel.WheelCollider.brakeTorque = Mathf.Abs(vInput) * brakeTorque;
//wheel.WheelCollider.motorTorque = 0;
}
if (isAccelerating)
{
// Apply torque to Wheel colliders that have "Motorized" enabled
if (wheel.motorized)
{
wheel.WheelCollider.motorTorque = vInput * currentMotorTorque;
}
wheel.WheelCollider.brakeTorque = 0;
}
if (isStopping)
{
// If the user is trying to go in the opposite direction
// apply brakes to all wheels
wheel.WheelCollider.brakeTorque = Mathf.Abs(vInput) * brakeTorque + autoBrake;
if (forwardSpeed < 0)
{
wheel.WheelCollider.brakeTorque = (Mathf.Abs(vInput) * brakeTorque + autoBrake) * 5;
}
}
}
// rewards
Transform currentCheckpoint = checkpoints[0].transform;
foreach (GameObject checkpoint in checkpoints)
{
bool isCollected = checkpoint.GetComponent<Checkpoint>().isCollected;
if (!isCollected)
{
currentCheckpoint = checkpoint.transform;
break;
}
}
float checkpintDistance = distanceToCheckpoint(currentCheckpoint);
float reward = (1 - Mathf.InverseLerp(0, distanceBetweenCheckpoints, checkpintDistance)) / 500;
totalReward += reward;
AddReward(reward);
float checkpointAngle = angleToCheckpoint(currentCheckpoint);
if (checkpointAngle > 0)
reward = (1 - Mathf.InverseLerp(0, 60, checkpointAngle)) / 2000;
else
reward = Mathf.InverseLerp(-60, 0, checkpointAngle) / 2000;
AddReward(reward);
if (checkpintDistance < 0.1f)
{
currentCheckpoint.GetComponent<Checkpoint>().isCollected = true;
stepsSinceCheckpoint = 0;
checkpointsReached += 1;
// If last checkpoint
if (currentCheckpoint == checkpoints[checkpoints.Count - 1].transform)
{
AddReward(10f);
EndEpisode();
}
//TODO fix variable names
AddReward(1f);
AddReward(-totalReward);
totalReward = 0;
}
currentStep += 1;
stepsSinceCheckpoint += 1;
if (stepsSinceCheckpoint >= maxStepsPerCheckpoint)
{
stepsSinceCheckpoint = 0;
EndEpisode();
}
// print(GetCumulativeReward());
}
public override void Heuristic(in ActionBuffers actionsOut)
{
var discreteActionsOut = actionsOut.DiscreteActions;
discreteActionsOut[0] = 2;
discreteActionsOut[1] = 2;
if (Input.GetAxis("Vertical") < -0.5)
discreteActionsOut[0] = 0;
if (Input.GetAxis("Vertical") > 0.5)
discreteActionsOut[0] = 1;
if (Input.GetAxis("Horizontal") < -0.5)
discreteActionsOut[1] = 0;
if (Input.GetAxis("Horizontal") > 0.5)
discreteActionsOut[1] = 1;
}
// finds distance from agent to closest point on the checkpoint line
float distanceToCheckpoint(Transform checkpoint)
{
var closestPoint = checkpoint.GetComponent<Collider>().ClosestPointOnBounds(transform.position);
var distanceToCheckpoint = Vector3.Distance(transform.position, closestPoint);
return distanceToCheckpoint;
}
float angleToCheckpoint(Transform checkpoint)
{
Vector3 checkpointDirection = checkpoint.localPosition - transform.localPosition;
float angle = Vector3.SignedAngle(transform.forward, checkpointDirection, Vector3.up);
return angle;
}
private void OnCollisionEnter(Collision other) {
// if (other.gameObject.tag == "NPC")
// {
// AddReward(0.1f);
// }
if (other.gameObject.tag == "Player")
{
AddReward(-0.1f);
}
}
}

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behaviors:
BensonV7:
trainer_type: ppo
hyperparameters:
# Hyperparameters common to PPO and SAC
batch_size: 50
buffer_size: 10240
learning_rate: 3.0e-4
learning_rate_schedule: linear
# PPO-specific hyperparameters
# Replaces the "PPO-specific hyperparameters" section above
beta: 5.0e-2
epsilon: 0.2
lambd: 0.95
num_epoch: 3
# Configuration of the neural network (common to PPO/SAC)
network_settings:
vis_encoder_type: simple
normalize: false
hidden_units: 128
num_layers: 2
# Trainer configurations common to all trainers
max_steps: 2.0e7
time_horizon: 64
summary_freq: 10000
keep_checkpoints: 5
checkpoint_interval : 500000
threaded: false
init_path: null
# # behavior cloning
# behavioral_cloning:
# demo_path: 'c:\Users\noahk\Documents\Unity projects\Racesm\Assets\Demonstrations\BensonV3M.demo'
# strength: 0.5
# # steps: 150000
# # batch_size: 512
# # num_epoch: 3
# # samples_per_update: 0
reward_signals:
# environment reward (default)
extrinsic:
strength: 1.0
gamma: 0.99
self_play:
window: 10
play_against_latest_model_ratio: 0.5
save_steps: 20000
swap_steps: 10000
team_change: 100000
# # curiosity module
# curiosity:
# strength: 0.02
# gamma: 0.99
# encoding_size: 256
# learning_rate: 3.0e-4
# # GAIL
# gail:
# strength: 0.5
# # gamma: 0.99
# # encoding_size: 128
# demo_path: 'c:\Users\noahk\Documents\Unity projects\Racesm\Assets\Demonstrations\BensonV3M.demo'
# # learning_rate: 3.0e-4
# # use_actions: false
# # use_vail: false

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default_settings: null
behaviors:
BensonV7:
trainer_type: ppo
hyperparameters:
batch_size: 50
buffer_size: 10240
learning_rate: 0.0003
beta: 0.05
epsilon: 0.2
lambd: 0.95
num_epoch: 3
shared_critic: false
learning_rate_schedule: linear
beta_schedule: linear
epsilon_schedule: linear
network_settings:
normalize: false
hidden_units: 128
num_layers: 2
vis_encode_type: simple
memory: null
goal_conditioning_type: hyper
deterministic: false
reward_signals:
extrinsic:
gamma: 0.99
strength: 1.0
network_settings:
normalize: false
hidden_units: 128
num_layers: 2
vis_encode_type: simple
memory: null
goal_conditioning_type: hyper
deterministic: false
init_path: null
keep_checkpoints: 5
checkpoint_interval: 500000
max_steps: 20000000
time_horizon: 64
summary_freq: 10000
threaded: false
self_play:
save_steps: 20000
team_change: 100000
swap_steps: 10000
window: 10
play_against_latest_model_ratio: 0.5
initial_elo: 1200.0
behavioral_cloning: null
env_settings:
env_path: null
env_args: null
base_port: 5005
num_envs: 1
num_areas: 1
seed: -1
max_lifetime_restarts: 10
restarts_rate_limit_n: 1
restarts_rate_limit_period_s: 60
engine_settings:
width: 84
height: 84
quality_level: 5
time_scale: 20
target_frame_rate: -1
capture_frame_rate: 60
no_graphics: false
environment_parameters: null
checkpoint_settings:
run_id: BensonV7
initialize_from: null
load_model: false
resume: false
force: true
train_model: false
inference: false
results_dir: results
torch_settings:
device: null
debug: false

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30
MLAgents/results/BensonV7/run_logs/training_status.json Normal file
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@@ -0,0 +1,30 @@
{
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2
MLAgents/train.bat
View File

@@ -1,2 +1,2 @@
mlagents-learn config/BensonV6.1.yaml --run-id=BensonV6.1 --force
mlagents-learn config/BensonV7.yaml --run-id=BensonV7 --force
@REM mlagents-learn config/BensonV5.yaml --run-id=BensonV5 --resume --env="C:\Users\noahk\Desktop\BensonV5\Racesm_L.exe" --no-graphics --num-envs=1

1
ProjectSettings/TagManager.asset
View File

@@ -5,6 +5,7 @@ TagManager:
serializedVersion: 2
tags:
- Wall
- NPC
layers:
- Default
- TransparentFX