Create app.py

app.py

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+# 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()