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· 제목 : Deep Reinforcement Learning with Python: With Pytorch, Tensorflow and Openai Gym (Paperback)
· 분류 : 외국도서 > 컴퓨터 > 인공지능(AI)
· ISBN : 9781484268087
· 쪽수 : 382쪽
· 분류 : 외국도서 > 컴퓨터 > 인공지능(AI)
· ISBN : 9781484268087
· 쪽수 : 382쪽
목차
Chapter 1: Deep Reinforcement Learning
Chapter Goal: Introduce the reader to field of reinforcement learning and setting the context of what they will learn in rest of the book
Sub -Topics
1. Deep reinforcement learning
2. Examples and case studies
3. Types of algorithms with mind-map
4. Libraries and environment setup
5. Summary
Chapter 2: Markov Decision Processes
Chapter Goal: Help the reader understand models, foundations on which all algorithms are built.
Sub - Topics
1. Agent and environment
2. Rewards
3. Markov reward and decision processes
4. Policies and value functions
5. Bellman equations
Chapter 3: Model Based Algorithms Using Dynamic Programming
Chapter Goal: Introduce reader to dynamic programming and related algorithms
Sub - Topics:
1. Introduction to OpenAI Gym environment2. Policy evaluation/prediction
3. Policy iteration and improvement
4. Generalised policy iteration
5. Value iteration
Chapter Goal: Introduce Reader to model free methods which form the basis for majority of current solutions
Sub - Topics:
1. Prediction and control with Monte Carlo methods
2. Exploration vs exploitation
3. TD learning methods
4. TD control
5. On policy learning using SARSA
6. Off policy learning using q-learning
Chapter 5: Function Approximation and Deep Learning in Reinforcement Learning
Chapter Goal: Help readers understand value function approximation and Deep Learning use in Reinforcement Learning.
1. Limitations to tabular methods studied so far
2. Value function approximation
3. Linear methods and features used
4. Non linear function approximation using deep Learning
Chapter 6: Deep Q - Learning
Chapter Goal: Help readers understand core use of deep learning in reinforcement learning. Deep q learning and many of its variants are introduced here with in depth code exercises.
1. Deep q-networks (DQN)
2. Issues in Naive DQN
3. Introduce experience replay and target networks
4. Double q-learning (DDQN)
5. Duelling DQN
6. Categorical 51-atom DQN (C51)
7. Quantile regression DQN (QR-DQN)
8. Hindsight experience replay (HER)
Chapter 7: Policy Gradient Algorithms
Chapter Goal: Introduce reader to concept of policy gradients and related theory. Gain in depth knowledge of common policy gradient methods through hands-on exercises
1. Policy gradient approach and its advantages
2. The policy gradient theorem
3. REINFORCE algorithm
4. REINFORCE with baseline5. Actor-critic methods
6. Advantage actor critic (A2C/A3C)
7. Proximal policy optimization (PPO)
8. Trust region policy optimization (TRPO)
Chapter Goal: Introduce reader to the trade offs between two approaches ways to connect together the two seemingly dissimilar approaches. Gain in depth knowledge of some land mark approaches.
1. Tradeoff between policy gradients and q-learning
2. The connection
3. Deep deterministic policy gradient (DDPG)
4. Twin delayed DDPG (TD3)
5. Soft actor critic (SAC)
Chapter 9: Combining Planning and Learning
Chapter Goal: Introduce reader to the scalable approaches which are sample efficient for scalable problems.
1. Model based reinforcement learning
2. Dyna and its variants
3. Guided policy search
4. Monte Carlo tree search (MCTS)
5. AlphaGo
Chapter 10: Exploration vs Exploitation Revisited
Chapter Goal: With the backdrop of having gone through most of the popular algorithms, readers are now introduced again to exploration vs exploitation dilemma, central to reinforcement learning.
1. Multi arm bandits
2. Upper confidence bound
3. Thompson sampling
