Spaces:
Sleeping
Sleeping
| def train(env, agent, episodes=50, batch_size=32): | |
| for ep in range(episodes): | |
| state = env.reset() | |
| total_reward = 0 | |
| done = False | |
| while not done: | |
| # 🎯 choose action | |
| action = agent.choose_action(state) | |
| # environment step | |
| next_state, reward, done = env.step(action) | |
| # 💾 store experience | |
| agent.remember(state, action, reward, next_state, done) | |
| # 🧠 learn from memory | |
| agent.learn(batch_size) | |
| # move forward | |
| state = next_state | |
| total_reward += reward | |
| print(f"Episode {ep+1}, Reward: {total_reward:.2f}, Epsilon: {agent.epsilon:.3f}") |