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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Advantage actor-critic (A2C)

Before moving on, first, let's recall the advantage function. The advantage function is defined as the difference between the Q function and the value function. We can express the advantage function as:

The advantage function tells us, in state s, how good action a is compared to the average actions.

In A2C, we compute the policy gradient with the advantage function. So, first, let's see how to compute the advantage function. We know that the advantage function is the difference between the Q function and the value function, that is, Q(s, a) – V(s), so we can use two function approximators (neural networks), one for estimating the Q function and the other for estimating the value function. Then, we can subtract the values of these two networks to get the advantage value. But this will definitely not be an optimal method and, computationally, it will be expensive.

So, we can approximate the Q value as:

But how...