Medium·2yAn Intuitive Introduction to Reinforcement Learning, Part I
An introductory guide to reinforcement learning using environments from the OpenAI Gymnasium Python package. It covers high-level concepts like Q-learning, Markov Decision Processes, state-value vs. action-value, and the balance between exploration and exploitation. Practical examples, such as navigating a frozen lake, are used to illustrate these concepts.
Medium·2yReinforcement Learning, Part 8: Feature State Construction
Reinforcement learning involves an agent learning optimal strategies in complex environments based on state rewards. This post discusses enhancing linear methods by incorporating more complex state feature interactions without leaving the linear optimization space. Methods such as polynomial features, Fourier basis, state aggregation, and radial basis functions are compared in terms of their effectiveness. Nonparametric function approximation methods are also introduced to address limitations in parametric approaches.
Machine Learning News·2yScalable Multi-Agent Reinforcement Learning Framework for Efficient Decision-Making in Large-Scale Systems
Researchers from Peking University and King’s College London developed a decentralized policy optimization framework for multi-agent systems, improving scalability and decision-making efficiency in large-scale AI systems by reducing communication and system complexity. The framework uses model learning to enhance policy optimization with limited data and employs localized models for accurate state and reward predictions. Tested in diverse scenarios like transportation and power systems, it demonstrated superior performance, significantly reducing communication costs while improving convergence and sample efficiency. This scalable MARL framework shows potential for applications in advanced traffic, energy, and pandemic management.