Book Image

Deep Reinforcement Learning with Python - Second Edition

By : Sudharsan Ravichandiran
Book Image

Deep Reinforcement Learning with Python - Second Edition

By: Sudharsan Ravichandiran

Overview of this book

With significant enhancements in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow 2 and the OpenAI Gym toolkit. In addition to exploring RL basics and foundational concepts such as Bellman equation, Markov decision processes, and dynamic programming algorithms, this second edition dives deep into the full spectrum of value-based, policy-based, and actor-critic RL methods. It explores state-of-the-art algorithms such as DQN, TRPO, PPO and ACKTR, DDPG, TD3, and SAC in depth, demystifying the underlying math and demonstrating implementations through simple code examples. The book has several new chapters dedicated to new RL techniques, including distributional RL, imitation learning, inverse RL, and meta RL. You will learn to leverage stable baselines, an improvement of OpenAI’s baseline library, to effortlessly implement popular RL algorithms. The book concludes with an overview of promising approaches such as meta-learning and imagination augmented agents in research. By the end, you will become skilled in effectively employing RL and deep RL in your real-world projects.
Table of Contents (22 chapters)
18
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Index

DQN with prioritized experience replay

We learned that in DQN, we randomly sample a minibatch of K transitions from the replay buffer and train the network. Instead of doing this, can we assign some priority to each transition in the replay buffer and sample the transitions that had high priority for learning?

Yes, but first, why do we need to assign priority for the transition, and how can we decide which transition should be given more priority than the others? Let's explore this more in detail.

The TD error is the difference between the target value and the predicted value, as shown here:

A transition that has a high TD error implies that the transition is not correct, and so we need to learn more about that transition to minimize the error. A transition that has a low TD error implies that the transition is already good. We can always learn more from our mistakes rather than only focusing on what we are already good at, right? Similarly, we can learn more...