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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Imagination augmented agents

Are you a fan of chess? If I asked you to play chess, how would you play it? Before moving any chess piece on the chessboard, you might imagine the consequences of moving a chess piece and move the chess piece that you think would help you to win the game. So, basically, before taking any action, we imagine the consequence and, if it is favorable, we proceed with that action, else we refrain from performing that action.

Similarly, Imagination Augmented Agents (I2As) are augmented with imagination. Before taking any action in an environment, the agent imagines the consequences of taking the action and if they think the action will provide a good reward, they will perform the action. The I2A takes advantage of both model-based and model-free learning. Figure 17.8 shows the architecture of I2As:

Figure 17.8: I2A architecture

As we can observe from Figure 17.8, I2A architecture has both model-based and model-free paths. Thus, the action...