Variance-Based Rewards for Approximate Bayesian Reinforcement Learning

Jonathan Sorg, Satinder Singh, Richard Lewis
Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:563-570, 2010.

Abstract

The explore–exploit dilemma is one of the central challenges in Reinforcement Learn- ing (RL). Bayesian RL solves the dilemma by providing the agent with information in the form of a prior distribution over envi- ronments; however, full Bayesian planning is intractable. Planning with the mean MDP is a common myopic approximation of Bayesian planning. We derive a novel reward bonus that is a function of the posterior distribution over environments, which, when added to the reward in planning with the mean MDP, re- sults in an agent which explores efficiently and effectively. Although our method is similar to existing methods when given an uninfor- mative or unstructured prior, unlike existing methods, our method can exploit structured priors. We prove that our method results in a polynomial sample complexity and empirically demonstrate its advantages in a structured exploration task.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR8-sorg10a, title = {Variance-Based Rewards for Approximate {B}ayesian Reinforcement Learning}, author = {Sorg, Jonathan and Singh, Satinder and Lewis, Richard}, booktitle = {Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence}, pages = {563--570}, year = {2010}, editor = {Grünwald, Peter and Spirtes, Peter}, volume = {R8}, series = {Proceedings of Machine Learning Research}, month = {08--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r8/main/assets/sorg10a/sorg10a.pdf}, url = {https://proceedings.mlr.press/r8/sorg10a.html}, abstract = {The explore–exploit dilemma is one of the central challenges in Reinforcement Learn- ing (RL). Bayesian RL solves the dilemma by providing the agent with information in the form of a prior distribution over envi- ronments; however, full Bayesian planning is intractable. Planning with the mean MDP is a common myopic approximation of Bayesian planning. We derive a novel reward bonus that is a function of the posterior distribution over environments, which, when added to the reward in planning with the mean MDP, re- sults in an agent which explores efficiently and effectively. Although our method is similar to existing methods when given an uninfor- mative or unstructured prior, unlike existing methods, our method can exploit structured priors. We prove that our method results in a polynomial sample complexity and empirically demonstrate its advantages in a structured exploration task.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Variance-Based Rewards for Approximate Bayesian Reinforcement Learning %A Jonathan Sorg %A Satinder Singh %A Richard Lewis %B Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2010 %E Peter Grünwald %E Peter Spirtes %F pmlr-vR8-sorg10a %I PMLR %P 563--570 %U https://proceedings.mlr.press/r8/sorg10a.html %V R8 %X The explore–exploit dilemma is one of the central challenges in Reinforcement Learn- ing (RL). Bayesian RL solves the dilemma by providing the agent with information in the form of a prior distribution over envi- ronments; however, full Bayesian planning is intractable. Planning with the mean MDP is a common myopic approximation of Bayesian planning. We derive a novel reward bonus that is a function of the posterior distribution over environments, which, when added to the reward in planning with the mean MDP, re- sults in an agent which explores efficiently and effectively. Although our method is similar to existing methods when given an uninfor- mative or unstructured prior, unlike existing methods, our method can exploit structured priors. We prove that our method results in a polynomial sample complexity and empirically demonstrate its advantages in a structured exploration task. %Z Reissued by PMLR on 04 October 2026.
APA
Sorg, J., Singh, S. & Lewis, R.. (2010). Variance-Based Rewards for Approximate Bayesian Reinforcement Learning. Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R8:563-570 Available from https://proceedings.mlr.press/r8/sorg10a.html. Reissued by PMLR on 04 October 2026.

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