Ravichandiran S Deep Reinforcement Learning with Python 2ed 2020 torrent

Textbook in PDF format

Master classic PL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow
With significant enhancement 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 RL and deep RL algorithms with TensorFlow 2 and the OpenAI Gym toolkit.
In addition to exploring reinforcement learning basics and foundational concepts like 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 in depth state-of-the-art algorithms such as DQN, TRPO, PPO and ACKTR, DDPG, TD3, and SAC, demystifying the underlying math and demonstrating implementations through simple code examples.
The book has several new chapters dedicated to new RL techniques including distributional reinforcement learning, 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