搜索结果: 1-6 共查到“理论统计学 reinforcement”相关记录6条 . 查询时间(0.065 秒)
This paper proposes an online tree-based Bayesian approach for reinforcement learning. For inference, we employ a generalised context tree model. This defines a distribution on multivariate Gaussian p...
Regret Bounds for Reinforcement Learning with Policy Advice
Regret Bounds Reinforcement LearningPolicy Advice
2013/6/13
In some reinforcement learning problems an agent may be provided with a set of input policies, perhaps learned from prior experience or provided by advisors. We present a reinforcement learning with p...
Efficient Reinforcement Learning for High Dimensional Linear Quadratic Systems
Efficient Reinforcement Learning High Dimensional Linear Quadratic Systems
2013/4/28
We study the problem of adaptive control of a high dimensional linear quadratic (LQ) system. Previous work established the asymptotic convergence to an optimal controller for various adaptive control ...
A Greedy Approximation of Bayesian Reinforcement Learning with Probably Optimistic Transition Model
Reinforcement Learning Uncertain Knowledge Probabilistic Reasoning Optimal Behavior in Polynomial Time
2013/5/2
Bayesian Reinforcement Learning (RL) is capable of not only incorporating domain knowledge, but also solving the exploration-exploitation dilemma in a natural way. As Bayesian RL is intractable except...
Monte-Carlo utility estimates for Bayesian reinforcement learning
Monte-Carlo estimates Bayesian reinforcement learning
2013/5/2
This paper introduces a set of algorithms for Monte-Carlo Bayesian reinforcement learning. Firstly, Monte-Carlo estimation of upper bounds on the Bayes-optimal value function is employed to construct ...
A survey of random processes with reinforcement
urn model urn scheme Pólya’s urn stochastic approximation dynamical system exchangeability Lyapunov function reinforced random walk ERRW VRRW learning agent-based model evolutionary game theory self-avoiding walk
2009/5/18
The models surveyed include generalized Polya urns, reinforced random walks, interacting urn models, and continuous reinforced processes. Emphasis is on methods and results, with sketches provided of ...