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Cost-Sensitive Exploration in Bayesian Reinforcement Learning
Dongho Kim · Kee-Eung Kim · Pascal Poupart

Thu Dec 06 02:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor

In this paper, we consider Bayesian reinforcement learning (BRL) where actions incur costs in addition to rewards, and thus exploration has to be constrained in terms of the expected total cost while learning to maximize the expected long-term total reward. In order to formalize cost-sensitive exploration, we use the constrained Markov decision process (CMDP) as the model of the environment, in which we can naturally encode exploration requirements using the cost function. We extend BEETLE, a model-based BRL method, for learning in the environment with cost constraints. We demonstrate the cost-sensitive exploration behaviour in a number of simulated problems.

Author Information

Dongho Kim (PROWLER.io Limited)
Kee-Eung Kim (KAIST)
Pascal Poupart (University of Waterloo)

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