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-...
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. ...
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 ...
Supervised learning: That thing is a “double bacon cheese burger”. (Labels, putting names to faces…) These algorithms learn the correlations between data instances and their labels; that is, they require a labelled dataset. Those labels are used to “supervise” and correct the algorithm as...
Theory: Starting from a uniform mathematical framework, this book derives the theory and algorithms of reinforcement learning, including the algorithms in large model era such as PPO, RLHF, IRL, and PbRL. Practice: Every chapter is accompanied by high quality implementation based on Python 3, Gy...