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.
Understand core RL concepts including the methodologies, math, and code
Train an agent to solve Blackjack, FrozenLake, and many other problems using OpenAI Gym
Train an agent to play Ms Pac-Man using a Deep Q Network
Learn policy-based, value-based, and actor-critic methods
Master the math behind DDPG, TD3, TRPO, PPO, and many others
Explore new avenues such as the distributional RL, meta RL, and inverse RL
Use Stable Baselines to train an agent to walk and play Atari games
Basic familiarity with linear algebra, calculus, and the Python programming language is required. Some experience with TensorFlow would be a plus.