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)
Other Books You May Enjoy


DAgger is one of the most-used imitation learning algorithms. Let's understand how DAgger works with an example. Let's revisit our example of training an agent to drive a car. First, we initialize an empty dataset .

In the first iteration, we start off with some policy to drive the car. Thus, we generate a trajectory using the policy . We know that the trajectory consists of a sequence of states and actions—that is, states visited by our policy and actions made in those states using our policy . Now, we create a new dataset by taking only the states visited by our policy and we use an expert to provide the actions for those states. That is, we take all the states from the trajectory and ask the expert to provide actions for those states.

Now, we combine the new dataset with our initialized empty dataset and update as:

Next, we train a classifier on this updated dataset and learn a new policy .

In the second iteration, we use the...