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)
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We started off the chapter by learning that with value-based methods, we extract the optimal policy from the optimal Q function (Q values). Then we learned that it is difficult to compute the Q function when our action space is continuous. We can discretize the action space; however, discretization is not always desirable, and it leads to the loss of several important features and an action space with a huge set of actions.

So, we resorted to the policy-based method. In the policy-based method, we compute the optimal policy without the Q function. We learned about one of the most popular policy-based methods called the policy gradient, in which we find the optimal policy directly by parameterizing the policy using some parameter .

We also learned that in the policy gradient method, we select actions based on the action probability distribution given by the network, and if we win the episode, that is, if we get a high return, then we assign high probabilities to all...