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)
Other Books You May Enjoy

How does an ANN learn?

If the cost or loss is very high, then it means that our network is not predicting the correct output. So, our objective is to minimize the cost function so that our neural network predictions will be better. How can we minimize the cost function? That is, how can we minimize the loss/cost? We learned that the neural network makes predictions using forward propagation. So, if we can change some values in the forward propagation, we can predict the correct output and minimize the loss. But what values can we change in the forward propagation? Obviously, we can't change input and output. We are now left with weights and bias values. Remember that we just initialized weight matrices randomly. Since the weights are random, they are not going to be perfect. Now, we will update these weight matrices (Wxh and Why) in such a way that our neural network gives a correct output. How do we update these weight matrices? Here comes a new technique called gradient...