Congratulations!

1. Congratulations!

Congratulations for completing this exciting but demanding journey in the field of Deep Reinforcement Learning!

2. Chapter 1

Let's look back on the steps we've made together. In chapter 1, we discovered how neural networks could approximate a value function and built our first value-based DRL algorithm: the Barebone version of DQN.

3. Chapter 2

Throughout the course of Chapter 2, we introduced layer upon layer of sophistication, implementing DQN and Double DQN; our work in the domain of value-based DRL culminated in the implementation of prioritized experience replay.

4. Chapter 3

In Chapter 3, we discovered policy gradient methods with REINFORCE and A2C. These techniques are conceptually complex, but very powerful as they enable DRL in continuous action spaces.

5. Chapter 4

Finally, in chapter 4, we introduced PPO, a popular, state-of-the art algorithm taking policy-gradient methods to new heights. We also explored different ways to structure the training loops for sophisticated policy gradient techniques; and to conclude we discovered how to automate hyperparameter tuning with Optuna.

6. What next?

Deep Reinforcement Learning is a vast and exciting field, and there is a lot that we haven't discussed. Advanced algorithms such as DDPG or Soft Actor Critic; topics such as multi-agent learning or model-based reinforcement learning; specific applications such as large language models with RLHF: those are all fascinating topics for another day!

7. Well done!

Once again, congratulations! And keep learning.

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

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