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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Chapter 12 – Learning DDPG, TD3, and SAC

  1. DDPG consists of an actor and critic. The actor is a policy network and uses the policy gradient method for learning the optimal policy. The critic is a DQN and it evaluates the action produced by the actor.
  2. The critic is basically a DQN. The goal of the critic is to evaluate the action produced by the actor network. The critic evaluates the action produced by the actor using the Q value computed by the DQN.
  3. The key features of TD3 includes clipped double Q learning, delayed policy updates, and target policy smoothing.
  4. Instead of using one critic network, we use two main critic networks for computing the Q value and we use two target critic networks for computing the target value. We compute two target Q values using two target critic networks and use the minimum value out of these two while computing the loss. This helps in preventing the overestimation of the target Q value.
  5. The DDPG method...