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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Chapter 15 – Imitation Learning and Inverse RL

  1. One of the simplest and most naive ways to perform imitation learning is by treating an imitation learning task as a supervised learning task. First, we collect a set of expert demonstrations, then we train a classifier to perform the same action performed by the expert in a particular state. We can view this as a big multiclass classification problem and train our agent to perform the action performed by the expert in the respective state.
  2. In DAgger, we aggregate the dataset over a series of iterations and train the classifier on the aggregated dataset.
  3. In DQfD, we fill the replay buffer with expert demonstrations and pre-train the agent. Note that these expert demonstrations are used only for pretraining the agent. Once the agent is pre-trained, the agent will interact with the environment and gather more experience and make use of it for learning. Thus DQfD consists of two phases, which are pre-training and...