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CAMEO: Curiosity Augmented Metropolis for Exploratory Optimal Policies

Abstract : Reinforcement Learning has drawn huge interest as a tool for solving optimal control problems. Solving a given problem (task or environment) involves converging towards an optimal policy. However, there might exist multiple optimal policies that can dramatically differ in their behaviour; for example, some may be faster than the others but at the expense of greater risk. We consider and study a distribution of optimal policies. We design a curiosity-augmented Metropolis algorithm (CAMEO), such that we can sample optimal policies, and such that these policies effectively adopt diverse behaviours, since this implies greater coverage of the different possible optimal policies. In experimental simulations we show that CAMEO indeed obtains policies that all solve classic control problems, and even in the challenging case of environments that provide sparse rewards. We further show that the different policies we sample present different risk profiles, corresponding to interesting practical applications in interpretability, and represents a first step towards learning the distribution of optimal policies itself.
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Contributor : Mohamed Alami Chehboune Connect in order to contact the contributor
Submitted on : Monday, May 23, 2022 - 11:25:57 AM
Last modification on : Wednesday, June 1, 2022 - 3:33:39 AM


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  • HAL Id : hal-03675575, version 1



Mohamed Alami Chehboune, Rim Kaddah, Luca Martino, Fernando Llorente, Jesse Read. CAMEO: Curiosity Augmented Metropolis for Exploratory Optimal Policies. EUSIPCO 2022, Aug 2022, Belgrade, Serbia. ⟨hal-03675575⟩



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