A2C with batch updates

In this course so far, you have been using variations around the same core DRL training loop. In practice, there are a number of ways in which this structure can be extended, for example to accommodate batch updates.

You will now revisit the A2C training loop on the Lunar Lander environment, but instead of updating the networks at every step, you will wait until 10 steps have elapsed before running the gradient descent step. By averaging the losses over 10 steps, you will benefit from slightly more stable updates.

This exercise is part of the course

Deep Reinforcement Learning in Python

Exercise instructions

Append the losses from each step to the loss tensors for the current batch.
Calculate the batch losses.
Reinitialize the loss tensors.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

actor_losses = torch.tensor([])
critic_losses = torch.tensor([])
for episode in range(10):
    state, info = env.reset()
    done = False
    episode_reward = 0
    step = 0
    while not done:
        step += 1
        action, action_log_prob = select_action(actor, state)                
        next_state, reward, terminated, truncated, _ = env.step(action)
        done = terminated or truncated
        episode_reward += reward
        actor_loss, critic_loss = calculate_losses(
            critic, action_log_prob, 
            reward, state, next_state, done)
        # Append to the loss tensors
        actor_losses = torch.cat((____, ____))
        critic_losses = torch.cat((____, ____))
        if len(actor_losses) >= 10:
            # Calculate the batch losses
            actor_loss_batch = actor_losses.____
            critic_loss_batch = critic_losses.____
            actor_optimizer.zero_grad(); actor_loss_batch.backward(); actor_optimizer.step()
            critic_optimizer.zero_grad(); critic_loss_batch.backward(); critic_optimizer.step()
            # Reinitialize the loss tensors
            actor_losses = ____
            critic_losses = ____
        state = next_state
    describe_episode(episode, reward, episode_reward, step)

Edit and Run Code

This exercise is part of the course

Deep Reinforcement Learning in Python

AdvancedSkill Level

4.8+

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Discover how deep reinforcement learning improves upon traditional Reinforcement Learning while studying and implementing your first Deep Q Learning algorithm.

Exercise 1: Introduction to deep reinforcement learning Exercise 2: Environment and neural network setup Exercise 3: DRL training loop Exercise 4: Introduction to deep Q learning Exercise 5: Deep learning and DQN Exercise 6: The Q-Network architecture Exercise 7: Instantiating the Q-Network Exercise 8: The barebone DQN algorithm Exercise 9: Barebone DQN action selection Exercise 10: Barebone DQN loss function Exercise 11: Training the barebone DQN

Dive into Deep Q-learning by implementing the original DQN algorithm, featuring Experience Replay, epsilon-greediness and fixed Q-targets. Beyond DQN, you will then explore two fascinating extensions that improve the performance and stability of Deep Q-learning: Double DQN and Prioritized Experience Replay.

Exercise 1: DQN with experience replay Exercise 2: The double-ended queue Exercise 3: Experience replay buffer Exercise 4: DQN with experience replay Exercise 5: The complete DQN algorithm Exercise 6: Epsilon-greediness Exercise 7: Fixed Q-targets Exercise 8: Implementing the complete DQN algorithm Exercise 9: Double DQN Exercise 10: Online network and target network in DDQN Exercise 11: Training the double DQN Exercise 12: Prioritized experience replay Exercise 13: Prioritized experience replay buffer Exercise 14: Sampling from the PER buffer Exercise 15: DQN with prioritized experience replay

Learn about the foundational concepts of policy gradient methods found in DRL. You will begin with the policy gradient theorem, which forms the basis for these methods. Then, you will implement the REINFORCE algorithm, a powerful approach to learning policies. The chapter will then guide you through Actor-Critic methods, focusing on the Advantage Actor-Critic (A2C) algorithm, which combines the strengths of both policy gradient and value-based methods to enhance learning efficiency and stability.

Exercise 1: Introduction to policy gradient Exercise 2: The policy network architecture Exercise 3: Working with discrete distributions Exercise 4: Policy gradient and REINFORCE Exercise 5: Action selection in REINFORCE Exercise 6: Training the REINFORCE algorithm Exercise 7: Advantage Actor Critic Exercise 8: Critic network Exercise 9: Actor Critic loss calculations Exercise 10: Training the A2C algorithm

Explore Proximal Policy Optimization (PPO) for robust DRL performance. Next, you will examine using an entropy bonus in PPO, which encourages exploration by preventing premature convergence to deterministic policies. You'll also learn about batch updates in policy gradient methods. Finally, you will learn about hyperparameter optimization with Optuna, a powerful tool for optimizing performance in your DRL models.

Exercise 1: Proximal policy optimization Exercise 2: The clipped probability ratio Exercise 3: The clipped surrogate objective function Exercise 4: Entropy bonus and PPO Exercise 5: Entropy playground Exercise 6: Training the PPO algorithm Exercise 7: Batch updates in policy gradient Exercise 8: Minibatch and DRL Exercise 9: A2C with batch updates

Current Exercise

Exercise 10: Hyperparameter optimization with Optuna Exercise 11: Hyperparameter or not?Exercise 12: Hands-on with Optuna Exercise 13: Congratulations!