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

Supervised imitation learning

In the imitation learning setting, our goal is to mimic the expert. Say, we want to train our agent to drive a car. Instead of training the agent from scratch by having them interact with the environment, we can train them with expert demonstrations. Okay, what are expert demonstrations? An expert demonstrations are a set of trajectories consisting of state-action pairs where each action is performed by the expert.

We can train an agent to mimic the actions performed by the expert in various respective states. Thus, we can view expert demonstrations as training data used to train our agent. The fundamental idea of imitation learning is to imitate (learn) the behavior of an expert.

One of the simplest and most naive ways to perform imitation learning is to treat the imitation learning task as a supervised learning task. First, we collect a set of expert demonstrations, and then we train a classifier to perform the same action performed by the...