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 9 – Deep Q Network and Its Variants

  1. When the environment consists of a large number of states and actions, it will be very expensive to compute the Q value of all possible state-action pairs in an exhaustive fashion. So, we use a deep Q network for approximating the Q function.
  2. We use a buffer called the replay buffer to collect the agent's experience and based on this experience, we train our network. The replay buffer is usually implemented as a queue structure (first in, first out) rather than a list. So, if the buffer is full and the new experience comes in, we remove the old experience and add the new experience into the buffer.
  3. When the target and predicted values depend on the same parameter , it will cause instability in the mean squared error and the network will learn poorly. It also causes a lot of divergence during training. So, we use a target network.
  4. Unlike with DQNs, in double DQNs, we compute the target value using two...