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: 我的感觉是在对强化学习的算法,包括model-based和model-free的算法,有了一定了解之后再来看这本书,能梳理清楚...
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...
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-...
Fundamental algorithms such as sorting or hashing are used trillions of times on any given day1. As demand for computation grows, it has become critical for these algorithms to be as performant as possible. Whereas remarkable progress has been achieved i
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 ...
technologies of intelligent agent.In the paper,we firstly introduce the model and foundation of RL,then,we deeply discuss the main algorithms of RL,including Sarsa,temporal difference,Q-learning and function approximation,finally,we briefly introduce some applications of RL and some ...
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 ...
RL theory comprises a formal framework for describing an agent interacting with its environment, together with a family of learning algorithms, and analyses of their performance. 4 2 The Reinforcement Learning Framework In the standard framework for RL, a learning agent—an animal or perhaps a ...