Titlebook: Reinforcement Learning; State-of-the-Art Marco Wiering,Martijn Otterlo Book 2012 Springer-Verlag Berlin Heidelberg 2012 Artificial Intellig

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沉思的魚

Hierarchical Approachestely and the results re-combined to find a solution to the original problem. It is well known that the na?ve application of reinforcement learning (RL) techniques fails to scale to more complex domains. This Chapter introduces hierarchical approaches to reinforcement learning that hold out the promi

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組成

Bayesian Reinforcement Learning prior distribution over unknown parameters and learning is achieved by computing a posterior distribution based on the data observed. Hence, Bayesian reinforcement learning distinguishes itself from other forms of reinforcement learning by explicitly maintaining a distribution over various quantiti

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Partially Observable Markov Decision Processes have had many successes. In many problem domains, however, an agent suffers from limited sensing capabilities that preclude it from recovering a Markovian state signal from its perceptions. Extending the MDP framework, partially observable Markov decision processes (POMDPs) allow for principled dec

思考

Predictively Defined Representations of State important information from the past into some sort of state variable. In this chapter, we start with a broad examination of the concept of state, with emphasis on the fact that there are many possible representations of state for a given dynamical system, each with different theoretical and computa

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Decentralized POMDPsl reward based on local information only. This means that agents do not observe a Markovian signal during execution and therefore the agents’ individual policies map fromhistories to actions. Searching for an optimal joint policy is an extremely hard problem: it is NEXP-complete. This suggests, assu

reception

Transfer in Reinforcement Learning: A Framework and a Survey improve its performance (e.g., by reducing the number of samples needed to achieve a nearly optimal performance). In this chapter we provide a formalization of the general transfer problem, we identify the main settings which have been investigated so far, and we review the most important approaches to transfer in reinforcement learning.

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1867-4534 Reinforcement Learning.Includes a survey of previous papers.Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptiv