The Reinforcement Learning Workshop
Alessandro Palmas Emanuele Ghelfi Dr. Alexandra Galina Petre Mayur Kulkarni Anand N.S. Quan Nguyen Aritra Sen Anthony So Saikat Basak更新时间：2021-06-11 18:38:06
最新章节：12. Evolutionary Strategies for RL封面
版权信息
Preface
1. Introduction to Reinforcement Learning
Introduction
Learning Paradigms
Fundamentals of Reinforcement Learning
Reinforcement Learning Frameworks
Applications of Reinforcement Learning
Summary
2. Markov Decision Processes and Bellman Equations
Introduction
Markov Processes
3. Deep Learning in Practice with TensorFlow 2
Introduction
An Introduction to TensorFlow and Keras
How to Implement a Neural Network Using TensorFlow
Simple Regression Using TensorFlow
Simple Classification Using TensorFlow
TensorBoard – How to Visualize Data Using TensorBoard
Summary
4. Getting Started with OpenAI and TensorFlow for Reinforcement Learning
Introduction
OpenAI Gym
OpenAI Universe – Complex Environment
TensorFlow for Reinforcement Learning
OpenAI Baselines
Training an RL Agent to Solve a Classic Control Problem
Summary
5. Dynamic Programming
Introduction
Solving Dynamic Programming Problems
Identifying Dynamic Programming Problems
Dynamic Programming in RL
Summary
6. Monte Carlo Methods
Introduction
The Workings of Monte Carlo Methods
Understanding Monte Carlo with Blackjack
Types of Monte Carlo Methods
Exploration versus Exploitation Trade-Off
Importance Sampling
Solving Frozen Lake Using Monte Carlo
Summary
7. Temporal Difference Learning
Introduction to TD Learning
TD(0) – SARSA and Q-Learning
N-Step TD and TD(λ) Algorithms
The Relationship between DP Monte-Carlo and TD Learning
Summary
8. The Multi-Armed Bandit Problem
Introduction
Formulation of the MAB Problem
The Python Interface
The Greedy Algorithm
The Explore-then-Commit Algorithm
The ε-Greedy Algorithm
The UCB algorithm
Thompson Sampling
Contextual Bandits
Summary
9. What Is Deep Q-Learning?
Introduction
Basics of Deep Learning
Basics of PyTorch
The Action-Value Function (Q Value Function)
Deep Q Learning
Challenges in DQN
Summary
10. Playing an Atari Game with Deep Recurrent Q-Networks
Introduction
Understanding the Breakout Environment
CNNs in TensorFlow
Combining a DQN with a CNN
RNNs in TensorFlow
Building a DRQN
Introduction to the Attention Mechanism and DARQN
Summary
11. Policy-Based Methods for Reinforcement Learning
Introduction
Policy Gradients
Deep Deterministic Policy Gradients
Improving Policy Gradients
Summary
12. Evolutionary Strategies for RL
Introduction
Problems with Gradient-Based Methods
Introduction to Genetic Algorithms
Summary
Appendix
1. Introduction to Reinforcement Learning
2. Markov Decision Processes and Bellman Equations
3. Deep Learning in Practice with TensorFlow 2
4. Getting started with OpenAI and TensorFlow for Reinforcement Learning
5. Dynamic Programming
6. Monte Carlo Methods
7. Temporal Difference Learning
8. The Multi-Armed Bandit Problem
9. What Is Deep Q-Learning?
10. Playing an Atari Game with Deep Recurrent Q-Networks
11. Policy-Based Methods for Reinforcement Learning
12. Evolutionary Strategies for RL
更新时间：2021-06-11 18:38:06