原文链接:Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation Abstract 强化学习非常适合用来优化推荐系统。当前的解决方案主要集中于无模型方法,需要与真实环境频繁交互,因此在模型学习方面成本高昂。离线评估方法(如重要性抽样)可以缓解此类限制,但通常需要大量的记录数据,并且在操作空间较...
这一章主要介绍训练好一组emsemble model后,如何进行planning,这里主要用到的是MPC方法(Model predictive control),MPC的优点是计算负担较低(没有梯度),并且不需要事先指定任务范围。 给定一个起始states_t,MPC控制器的预测范围T,以及一个动作序列a_{t: t+T} \doteq\left\{a_{t}, \ldots, a_{t+T}\r...
最后Implicit Model-based Reinforcement Learning这部分,提出了一个隐式学习的观点,比如整个问题都可以看做是model free方法,里面的各个模块只是来解决这个问题的隐式方法,我们并不需要作区分(In other words, the entire model based RL procedure (model learning, planning, and possibly integration in value/policy...
Model-Based Reinforcement Learning ( Day 1 : Introduction )Littman, Michael L
While model-based reinforcement learning has empirically been shown to significantly reduce the sample complexity that hinders model-free RL, the theoretical understanding of such methods has been rather limited. In this paper, we introd... Y Luo,H Xu,Y Li,... 被引量: 3发表: 2018年 LF_00...
Spike-Based Reinforcement Learning in Continuous State and Action Space: When Policy Gradient Methods Fail Changes of synaptic connections between neurons are thought to be the physiological basis of learning. These changes can be gated by neuromodulators that e... E Vasilaki,N Frémaux,R Urbanczi...
Model-based reinforcement learning approach for planning in self-adaptive software system. Policy-based adaptation is one of interesting topics in self-adaptive software research community. Current works in the field proposed the term of policy e... HN Ho,E Lee - ACM 被引量: 4发表: 2015年 Al...
Summary: Reinforcement Learning (RL)-based online approximate optimal control methods applied to deterministic systems typically require a restrictive Persistence of Excitation (PE) condition for convergence. This paper develops a Concurrent Learning (CL)-based implementation of model-based RL to solve app...
文章要点:这篇文章提出了一个Model-Based Model-Free (MBMF)算法,通过学习一个dynamics model然后作为先验来做model free optimization,这里的model free optimization指的是基于Gaussian Process (GP) 的Bayesian Optimization (BO)。 具体的,如果dynamics model是未知的,就先学一个 ...
In this work we explore the application of the Reinforcement Learning (RL) paradigm to study the autonomous development of robot controllers without a priori supervised learning. Such a model-based RL architecture is discussed for the cognitive implications of applying RL in humanoid robots. To this...