Titlebook: Recent Advances in Reinforcement Learning; 8th European Worksho Sertan Girgin,Manuel Loth,Daniil Ryabko Conference proceedings 2008 Springe

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A Near Optimal Policy for Channel Allocation in Cognitive Radio,P). In this contribution, we consider a previously proposed model for a channel allocation task and develop an approach to compute a near optimal policy. The proposed method is based on approximate (point based) value iteration in a continuous state Markov Decision Process (MDP) which uses a specifi

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Basis Expansion in Natural Actor Critic Methods, goal by directly approximating the policy using a parametric function approximator; the expected return of the current policy is estimated and its parameters are updated by steepest ascent in the direction of the gradient of the expected return with respect to the policy parameters. In general, the

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Optimistic Planning of Deterministic Systems, from that state and using any sequence of actions. This forms a tree whose size is exponential in the planning time horizon. Here we ask the question: given finite computational resources (e.g. CPU time), which may not be known ahead of time, what is the best way to explore this tree, such that onc

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Tile Coding Based on Hyperplane Tiles,nction approximator that has been successfully applied to many reinforcement learning tasks. In this paper we introduce the hyperplane tile coding, in which the usual tiles are replaced by parameterized hyperplanes that approximate the action-value function. We compared the performance of hyperplane

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Applications of Reinforcement Learning to Structured Prediction,ructured outputs such as sequences, trees or graphs. When predicting such structured data, learning models have to select solutions within very large discrete spaces. The combinatorial nature of this problem has recently led to learning models integrating a search component..In this paper, we show t