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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Is the MC method applicable to all tasks?

We learned that Monte Carlo is a model-free method, and so it doesn't require the model dynamics of the environment to compute the value and Q function in order to find the optimal policy. The Monte Carlo method computes the value function and Q function by just taking the average return of the state and the average return of the state-action pair, respectively.

But one issue with the Monte Carlo method is that it is applicable only to episodic tasks. We learned that in the Monte Carlo method, we compute the value of the state by taking the average return of the state and the return is the sum of rewards of the episode. But when there is no episode, that is, if our task is a continuous task (non-episodic task), then we cannot apply the Monte Carlo method.

Okay, how do we compute the value of the state where we have a continuous task and also where we don't know the model dynamics of the environment? Here is where...