DRL training loop

To allow the agent to experience the environment repeatedly, you need to set up a training loop.

Many DRL algorithms have in common this core structure:

Loop through episodes
Loop through steps within each episode
At each step, choose an action, calculate the loss, and update the network

You are provided with placeholder select_action() and calculate_loss() functions that allow the code to run. The Network and optimizer defined from the previous exercise are also available to you.

This exercise is part of the course

Deep Reinforcement Learning in Python

Exercise instructions

Ensure that the outer loop (over episodes) runs for ten episodes.
Ensure that the inner loop (over steps) runs until the episode is complete.
Take the action selected by select_action() in the env environment.
At the end of an inner loop iteration, update the state before starting the next step.

Hands-on interactive exercise

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

env = gym.make("LunarLander-v2")
# Run ten episodes
for episode in ____:
    state, info = env.reset()
    done = False    
    # Run through steps until done
    while ____:
        action = select_action(network, state)        
        # Take the action
        next_state, reward, terminated, truncated, _ = ____
        done = terminated or truncated        
        loss = calculate_loss(network, state, action, next_state, reward, done)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()        
        # Update the state
        state = ____
    print(f"Episode {episode} complete.")

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

Current Exercise

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