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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Introducing TensorBoard

TensorBoard is TensorFlow's visualization tool, which can be used to visualize a computational graph. It can also be used to plot various quantitative metrics and the results of several intermediate calculations. When we are training a really deep neural network, it becomes confusing when we have to debug the network. So, if we can visualize the computational graph in TensorBoard, we can easily understand such complex models, debug them, and optimize them. TensorBoard also supports sharing.

As shown in Figure 8.2, the TensorBoard panel consists of several tabs—SCALARS, IMAGES, AUDIO, GRAPHS, DISTRIBUTIONS, HISTOGRAMS, and EMBEDDINGS:

Figure 8.2: TensorBoard

The tabs are pretty self-explanatory. The SCALARS tab shows useful information about the scalar variables we use in our program. For example, it shows how the value of a scalar variable called loss changes over several iterations.

The GRAPHS tab shows the computational graph...