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...
Returns at successive time steps are related to each other in a way that is important for the theory and algorithms of reinforcement learning:G_{t}\doteq R_{t+1}+\gamma G_{t+1} 假如奖励为常数+1,则折扣回报为:G_{t}=\sum_{k=0}^\infty\gamma^{k}=\frac{1}{1-\gamma}. 3.4 Uni...
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...
learning algorithms in a reliable and stable manner, and finally (iv) how to integrate the study of reinforcement learning into the rich theory of ... S Mahadevan,B Liu,P Thomas,... - 《Computer Science》 被引量: 28发表: 2014年 Instance-based Policy Learning by Real-coded Genetic Algori...
Reinforcement Learning Algorithms: Acceleration Design and Non-Asymptotic TheoryArtificial intelligence.Computer science.Reinforcement learning (RL) aims to design strategies for an agent to find a desirable policy through interacting with an environment in order to maximize an accumulative reward for a ...
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-...
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 ...
The purpose of the book is to give an overview of the Reinforcement Learning (RL) methodology, with a particular focus on problems of optimal and suboptimal control, as well as discrete optimization. Reinforcement Learning: Theory and Algorithms (Alekh Agarwal) The purpose of the course is to...
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 ...
Reinforcement learning is also used in operations research, information theory, game theory, control theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics, genetic algorithms and ongoing industrial automation efforts. ...