/Deep-Reinforcement-Learning-With-Python

Master classic RL, deep RL, distributional RL, inverse RL, and more using OpenAI Gym and TensorFlow with extensive Math

Primary LanguageJupyter Notebook

Master classic RL, deep RL, distributional RL, inverse RL, and more using OpenAI Gym and TensorFlow with extensive Math

About the book

Book Cover

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 completely revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow and the OpenAI Gym toolkit.

In addition to exploring RL basics and foundational concepts such as the Bellman equation, Markov decision processes, and dynamic programming, this second edition dives deep into the full spectrum of value-based, policy-based, and actor- critic RL methods with detailed math. 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 implement popular RL algorithms effortlessly. The book concludes with an overview of promising approaches such as meta-learning and imagination augmented agents in research.

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Table of Contents

Download the detailed and complete table of contents from here.

Free download the chapter 1 here.

6. Case Study: The MAB Problem

  • 6.1. The MAB Problem
  • 6.2. Creating Bandit in the Gym
  • 6.3. Epsilon-Greedy
  • 6.4. Implementing Epsilon-Greedy
  • 6.5. Softmax Exploration
  • 6.6. Implementing Softmax Exploration
  • 6.7. Upper Confidence Bound
  • 6.8. Implementing UCB
  • 6.9. Thompson Sampling
  • 6.10. Implementing Thompson Sampling
  • 6.11. Applications of MAB
  • 6.12. Finding the Best Advertisement Banner using Bandits
  • 6.13. Contextual Bandits
  • 7.1. Biological and artifical neurons
  • 7.2. ANN and its layers
  • 7.3. Exploring activation functions
  • 7.4. Forward and backward propgation in ANN
  • 7.5. Building neural network from scratch
  • 7.6. Recurrent neural networks
  • 7.7. LSTM-RNN
  • 7.8. Convolutional neural networks
  • 7.9. Generative adversarial networks
  • 13.1 Trust Region Policy Optimization
  • 13.2. Math Essentials
  • 13.3. Designing the TRPO Objective Function
  • 13.4. Solving the TRPO Objective Function
  • 13.5. Algorithm - TRPO
  • 13.6. Proximal Policy Optimization
  • 13.7. PPO with Clipped Objective
  • 13.9. Implementing PPO-Clipped Method
  • 13.10. PPO with Penalized Objective
  • 13.11. Actor Critic using Kronecker Factored Trust Region
  • 13.12. Math Essentials
  • 13.13. Kronecker-Factored Approximate Curvature (K-FAC)
  • 13.14. K-FAC in Actor Critic
  • 14.1. Why Distributional Reinforcement Learning?
  • 14.2. Categorical DQN
  • 14.3. Playing Atari games using Categorical DQN
  • 14.4. Quantile Regression DQN
  • 14.5. Math Essentials
  • 14.6. Understanding QR-DQN
  • 14.7. Distributed Distributional DDPG
  • 17.1. Meta Reinforcement Learning
  • 17.2. Model Agnostic Meta Learning
  • 17.3. Understanding MAML
  • 17.4. MAML in the Supervised Learning Setting
  • 17.5. Algorithm - MAML in Supervised Learning
  • 17.6. MAML in the Reinforcement Learning Setting
  • 17.7. Algorithm - MAML in Reinforcement Learning
  • 17.8. Hierarchical Reinforcement Learning
  • 17.9. MAXQ Value Function Decomposition
  • 17.10. Imagination Augmented Agents