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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Prediction and control tasks

In reinforcement learning, we perform two important tasks, and they are:

  • The prediction task
  • The control task

Prediction task

In the prediction task, a policy is given as an input and we try to predict the value function or Q function using the given policy. But what is the use of doing this? Our goal is to evaluate the given policy. That is, we need to determine whether the given policy is good or bad. How can we determine that? If the agent obtains a good return using the given policy then we can say that our policy is good. Thus, to evaluate the given policy, we need to understand what the return the agent would obtain if it uses the given policy. To obtain the return, we predict the value function or Q function using the given policy.

That is, we learned that the value function or value of a state denotes the expected return an agent would obtain starting from that state following some policy . Thus, by predicting...