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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19
Index

TD learning

The TD learning algorithm was introduced by Richard S. Sutton in 1988. In the introduction of the chapter, we learned that the reason the TD method became popular is that it combines the advantages of DP and the MC method. But what are those advantages?

First, let's recap quickly the advantages and disadvantages of DP and the MC method.

Dynamic programming—The advantage of the DP method is that it uses the Bellman equation to compute the value of a state. That is, we have learned that according to the Bellman equation, the value of a state can be obtained as the sum of the immediate reward and the discounted value of the next state. This is called bootstrapping. That is, to compute the value of a state, we don't have to wait till the end of the episode, instead, using the Bellman equation, we can estimate the value of a state just based on the value of the next state, and this is called bootstrapping.

Remember how we estimated the...