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|Title:||Monte Carlo Bayesian reinforcement learning|
|Source:||Wang, Y.,Won, K.S.,Hsu, D.,Lee, W.S. (2012). Monte Carlo Bayesian reinforcement learning. Proceedings of the 29th International Conference on Machine Learning, ICML 2012 2 : 1135-1142. ScholarBank@NUS Repository.|
|Abstract:||Bayesian reinforcement learning (BRL) encodes prior knowledge of the world in a model and represents uncertainty in model parameters by maintaining a probability distribution over them. This paper presents Monte Carlo BRL (MC-BRL), a simple and general approach to BRL. MC-BRL samples a priori a finite set of hypotheses for the model parameter values and forms a discrete partially observable Markov decision process (POMDP) whose state space is a cross product of the state space for the reinforcement learning task and the sampled model parameter space. The POMDP does not require conjugate distributions for belief representation, as earlier works do, and can be solved relatively easily with pointbased approximation algorithms. MC-BRL naturally handles both fully and partially observable worlds. Theoretical and experimental results show that the discrete POMDP approximates the underlying BRL task well with guaranteed performance. Copyright 2012 by the author(s)/owner(s).|
|Source Title:||Proceedings of the 29th International Conference on Machine Learning, ICML 2012|
|Appears in Collections:||Staff Publications|
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