Title: Reinforcement Learning: Theory and Algorithms Author(s) Alekh Agarwal, Nan Jiang, Sham M. Kakade, Wen Sun Publisher: Self-Publishing (2022) Paperback: N/A eBook: PDF Language: English ISBN-10: N/A ISBN-
最近在了解Model-based RL的一些理论结果和发展过程,发现想了解的推导方法和理论基础大多在Reinforcement Learning: Theory and Algorithms一书中可以找到,该书现在还在草稿阶段,同时有一门课程配套,链接如下: Reinforcement Learning: Theory and Algorithmsrltheorybook.github.io/ 我的感觉是在对强化学习的算法,包括...
Our theoretical analysis begins with extending the fully independent systems41,70 to more realistic and general multi-agent systems, and expanding single-agent model learning theory54 to multi-agent systems. Empirically, we evaluate our algorithms in highly realistic simulators and real-world scenarios ...
This chapter lays out basic reinforcement learning theory. It introduces the notation used in reinforcement learning literature and provides detailed explanation and proofs of underlying concepts. It provides the foundation for reinforcement learning algorithms introduced in the next chapter.Ahlawat, Samit...
Returns at successive time steps are related to each other in a way that is important for the theory and algorithms of reinforcement learning: Note that this works for all time steps t < T , even if termination occurs at t + 1, if we define GT = 0. This often makes it easy to com...
现工作于OPENAI）3.《Reinforcement Learning: Theory and Algorithms》的作者，Alekh Agarwal, Nan Jiang,...
There is and has been a fruitful flow of concepts and ideas between studies of learning in biological and artificial systems. Much early work that led to the development of reinforcement learning (RL) algorithms for artificial systems was inspired by learning rules first developed in biology by Bu...
We also introduce several significant but challenging applications of these algorithms. Orthogonal to the existing reviews on MARL, we highlight several new angles and taxonomies of MARL theory, including learning in extensive-form games, decentralized MARL with networked agents, MARL in the mean-...
Reinforcement learning refers to goal-oriented algorithms, which learn how to attain a complex objective (goal) or maximize along a particular dimension over many steps.
All these methods are the roots of the modern RL theory and algorithms. DP is an optimization technique that breaks down the optimization task into subtasks utilizing the idea of recursion to obtain the solution. Stochastic optimal control problems can be solved using the DP method, but the ...