LNAI 2903 Model Based Reinforcement Learning for Alternating Markov Games 1st Edition by Drew Mellor ISBN 9783540206460 354020646X

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ISBN 10: 354020646X
ISBN 13: 9783540206460
Author: Drew Mellor

Online training is a promising technique for training reinforcement learning agents to play strategy board games over the internet against human opponents. But the limited training experience that can be generated by playing against real humans online means that learning must be data-efficient. Data-efficiency has been achieved in other domains by augmenting reinforcement learning with a model: model-based reinforcement learning. In this paper the Minimax-MBTD algorithm is presented, which extends model-based reinforcement learning to deterministic alternating Markov games, a generalisation of two-player zero-sum strategy board games like chess and Go. By using a minimax measure of optimality the strategy learnt generalises to arbitrary opponents, unlike approaches that explicitly model specific opponents. Minimax-MBTD is applied to Tic-Tac-Toe and found to converge faster than direct reinforcement learning, but focussing planning on successors to the current state resulted in slower convergence than unfocussed random planning.

LNAI 2903 Model Based Reinforcement Learning for Alternating Markov Games 1st Edition Table of contents:

Chapter 1: Introduction to Reinforcement Learning and Markov Games

• 1.1. The Basics of Reinforcement Learning
• 1.2. Key Concepts in Markov Decision Processes (MDPs)
• 1.3. Markov Games: Multi-Agent Extensions of MDPs
• 1.4. Alternating Markov Games: Definition and Characteristics
• 1.5. Importance of Model-Based Approaches in Reinforcement Learning
• 1.6. Overview of the Book

Chapter 2: Fundamentals of Markov Games and Game Theory

• 2.1. Introduction to Game Theory in Multi-Agent Systems
• 2.2. Single-Agent vs. Multi-Agent Environments
• 2.3. Markov Games: Formal Definition
• 2.4. Nash Equilibria and Solution Concepts for Markov Games
• 2.5. Applications of Markov Games in Economics, Robotics, and AI

Chapter 3: Model-Based Reinforcement Learning

• 3.1. Overview of Model-Free vs. Model-Based RL
• 3.2. Learning a Model of the Environment: Key Concepts
• 3.3. Planning and Decision Making with Learned Models
• 3.4. Model-Based RL Algorithms: Dyna-Q, PILCO, MBMF
• 3.5. Challenges in Model Learning and the Curse of Dimensionality

Chapter 4: Alternating Markov Games: Theoretical Foundations

• 4.1. The Structure of Alternating Markov Games
• 4.2. State and Action Spaces in Alternating Games
• 4.3. Sequential Decision Making with Alternating Turns
• 4.4. Key Challenges in Alternating Markov Games
• 4.5. Equilibrium and Policy Optimization in Alternating Markov Games

Chapter 5: Solving Alternating Markov Games with Model-Based RL

• 5.1. Formulating Alternating Markov Games in a Model-Based RL Framework
• 5.2. Value Iteration and Policy Iteration for Alternating Markov Games
• 5.3. Representing the Environment and Learning the Transition Model
• 5.4. Learning and Planning for Multiple Agents with Alternating Roles
• 5.5. Dynamic Programming Approaches in Alternating Markov Games

Chapter 6: Exploration and Exploitation in Alternating Markov Games

• 6.1. Exploration-Exploitation Tradeoff in Multi-Agent Systems
• 6.2. Exploration Strategies in Model-Based RL
• 6.3. Exploitation in Alternating Markov Games
• 6.4. Balancing Exploration and Exploitation in Alternating Turns
• 6.5. Case Study: Exploration in Two-Agent Games

Chapter 7: Advanced Topics in Model-Based RL for Alternating Markov Games

• 7.1. Partially Observable Alternating Markov Games (POAMGs)
• 7.2. Multi-Agent Coordination and Cooperation in Alternating Games
• 7.3. Stochasticity and Uncertainty in Alternating Markov Games
• 7.4. Deep Reinforcement Learning in Alternating Markov Games
• 7.5. Adversarial Agents and Game-Theoretic Approaches

Chapter 8: Applications of Model-Based RL in Alternating Markov Games

• 8.1. Robotics: Cooperative and Competitive Multi-Agent Systems
• 8.2. Game Theory and Economics: Auctions, Negotiations, and Markets
• 8.3. Autonomous Vehicles: Intersection Control and Traffic Management
• 8.4. Strategy Games and Simulations: AI Agents in Competitive Environments
• 8.5. Real-World Case Studies and Success Stories

Chapter 9: Experimental Setup and Empirical Results

• 9.1. Benchmark Environments for Testing Alternating Markov Games
• 9.2. Experimental Design for Model-Based RL in Alternating Markov Games
• 9.3. Simulation Results: Comparing Model-Based and Model-Free Approaches
• 9.4. Evaluating the Efficiency of Learning and Planning Algorithms
• 9.5. Insights from Empirical Experiments

Chapter 10: Future Directions and Challenges

• 10.1. Open Problems in Model-Based RL for Multi-Agent Systems
• 10.2. Scaling to Larger State and Action Spaces
• 10.3. Real-Time Learning and Decision Making in Alternating Markov Games
• 10.4. Future Trends in AI and Reinforcement Learning
• 10.5. Concluding Remarks: The Future of Multi-Agent Learning in Games

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