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Tobias Jung and Peter Stone.
**Feature Selection for Value Function Approximation Using Bayesian Model Selection**. In *The European Conference on
Machine Learning and Principles and Practice of Knowledge Discovery in Databases*, September 2009.

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Feature selection in reinforcement learning (RL), i.e. choosing basis functions such that useful approximations of the unknown value function can be obtained, is one of the main challenges in scaling RL to real-world applications. Here we consider the Gaussian process based framework GPTD for approximate policy evaluation, and propose feature selection through marginal likelihood optimization of the associated hyperparameters. Our approach has two appealing benefits: (1) given just sample transitions, we can solve the policy evaluation problem fully automatically (without looking at the learning task, and, in theory, independent of the dimensionality of the state space), and (2) model selection allows us to consider more sophisticated kernels, which in turn enable us to identify relevant subspaces and eliminate irrelevant state variables such that we can achieve substantial computational savings and improved prediction performance.

@InProceedings{ECML09-jung, author="Tobias Jung and Peter Stone", title="Feature Selection for Value Function Approximation Using Bayesian Model Selection", booktitle="The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases", month="September", year="2009", abstract={Feature selection in reinforcement learning (RL), i.e. choosing basis functions such that useful approximations of the unknown value function can be obtained, is one of the main challenges in scaling RL to real-world applications. Here we consider the Gaussian process based framework GPTD for approximate policy evaluation, and propose feature selection through marginal likelihood optimization of the associated hyperparameters. Our approach has two appealing benefits: (1) given just sample transitions, we can solve the policy evaluation problem fully automatically (without looking at the learning task, and, in theory, independent of the dimensionality of the state space), and (2) model selection allows us to consider more sophisticated kernels, which in turn enable us to identify relevant subspaces and eliminate irrelevant state variables such that we can achieve substantial computational savings and improved prediction performance. }, }

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