app.py

# 1. Import Dependencies
#!pip install gym[box2d] pyglet==1.3.2

import gym 
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import VecFrameStack
from stable_baselines3.common.evaluation import evaluate_policy
import os

# 2. Test Environment
environment_name = "CarRacing-v0"
env = gym.make(environment_name)

episodes = 5
for episode in range(1, episodes+1):
    state = env.reset()
    done = False
    score = 0 
    
    while not done:
        env.render()
        action = env.action_space.sample()
        n_state, reward, done, info = env.step(action)
        score+=reward
    print('Episode:{} Score:{}'.format(episode, score))
env.close()

env.close()

# 3. Train Model
log_path = os.path.join('Training', 'Logs')
model = PPO("CnnPolicy", env, verbose=1, tensorboard_log=log_path)
model.learn(total_timesteps=40000)

# 4. Save Model
ppo_path = os.path.join('Training', 'Saved Models', 'PPO_Driving_model')
model.save(ppo_path)

# 5. Evaluate and Test
evaluate_policy(model, env, n_eval_episodes=10, render=True)
env.close()
obs = env.reset()
while True:
    action, _states = model.predict(obs)
    obs, rewards, dones, info = env.step(action)
    env.render()
env.close()