Length7h 56m
About this audiobook
Reinforcement Learning: A Practical Guide to Algorithms delves into the impactful world of reinforcement learning, a key branch of AI. Spanning over five decades, reinforcement learning has significantly advanced AI, offering solutions for planning, budgeting, and strategic decision-making. This book provides a comprehensive understanding of reinforcement learning, focusing on building smart models and agents that adapt to changing requirements.
We cover fundamental and advanced topics, including value-based methods like UCB, SARSA, and Q-learning, as well as function approximation techniques. Additionally, we explore artificial neural networks, LSTD, gradient methods, emphatic TD methods, average reward methods, and policy gradient methods.
With clear explanations, diagrams, and examples, this book ensures that readers can grasp and apply reinforcement learning algorithms to real-world problems effectively. By the end, you will have a solid foundation in both theoretical and practical aspects of reinforcement learning.
Audiobook details
GenreTechnology, Science and Nature
Length7 hrs 56 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
Publish dateJan 3, 2025
LanguageEnglish
Table of contents
1Part-1
2Tabular Solution Methods
3Part-2
4Approximate Solution Methods
5Chapter 1. Introduction
Show all chaptersShow less
61.1 Reinforcement Learning
71.2 Examples
81.3 Elements of RL
91.4 Applications of RL
101.5 Summary
111.6 Questions
12Chapter 2. Multi-arm Bandits
132.1 An n-armed bandit problem
142.2 Action-value methods
152.3 Incremental implementation
162.4 Tracking a nonstationary problem
172.5 Optimistic Initial Values
182.6 Upper-Confidence-Bound-Action Selection
192.7 Gradient Bandit Algorithms
202.8 Associative Search (Contextual Bandits)
212.9 Summary
222.10 Questions
23Chapter 3. Solving Problems with Dynamic Programming
243.1 MDP
253.2 Categorizing RL algorithms
263.3 Dynamic Programming
273.4 Summary
283.5 Questions
29Chapter 4. Monte Carlo Methods
304.1 Monte Carlo prediction
314.2 Monte Carlo estimation of action values
324.3 Monte Carlo Control
334.4 Monte Carlo Control without Exploring
34Starts
354.5 Off policy Prediction via Importance Sampling
364.6 Incremental Implementation
374.7 Off-policy MC Control
384.8 Discounting-aware Importance sampling
394.9 Per-decision Importance Sampling
404.10 Summary
414.11 Questions
42Chapter 5. Temporal-Difference Learning
435.1 TD Prediction
445.2 Advantages of TD Prediction methods
455.3 Optimality of TD(0)
465.4 SARSA: On-policy TD Control
475.5 Q-learning: off-policy TD Control
485.6 Expected SARSA
495.7 Maximization Bias and Double Learning
505.7 Summary
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