Cnn, Neural Networks, Rnn, Machine Learning, Artificial Intelligence.
for episode in range(1000): state = env.reset() done = False while not done: action_probs = model.predict(state.reshape(1, -1))[0] action = np.random.choice(env.action_space.n, p=action_probs) next_state, reward, done, _ = env.step(action) # Update the agent's policy using PPO training #