Posterior weighted reinforcement learning with state uncertainty

Tobias Larsen, David S. Leslie, EJ Collins, Rafal Bogacz

Research output: Contribution to journalArticle (Academic Journal)peer-review

7 Citations (Scopus)


Reinforcement learning models generally assume that a stimulus is presented that allows a learner to unambiguously identify the state of nature, and the reward received is drawn from a distribution that depends on that state. However, in any natural environment, the stimulus is noisy. When there is state uncertainty, it is no longer immediately obvious how to perform reinforcement learning, since the observed reward cannot be unambiguously allocated to a state of the environment. This letter addresses the problem of incorporating state uncertainty in reinforcement learning models. We show that simply ignoring the uncertainty and allocating the reward to the most likely state of the environment results in incorrect value estimates. Furthermore, using only the information that is available before observing the reward also results in incorrect estimates. We therefore introduce a new technique, posterior weighted reinforcement learning, in which the estimates of state probabilities are updated according to the observed rewards (e.g., if a learner observes a reward usually associated with a particular state, this state becomes more likely). We show analytically that this modified algorithm can converge to correct reward estimates and confirm this with numerical experiments. The algorithm is shown to be a variant of the expectation-maximization algorithm, allowing rigorous convergence analyses to be carried out. A possible neural implementation of the algorithm in the cortico-basal-ganglia-thalamic network is presented, and experimental predictions of our model are discussed.
Translated title of the contributionPosterior weighted reinforcement learning with state uncertainty
Original languageEnglish
Pages (from-to)1149 - 1179
Number of pages31
JournalNeural Computation
Issue number5
Early online date7 Apr 2010
Publication statusPublished - May 2010

Bibliographical note

Publisher: MIT
Article number

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