Robust Reinforcement Learning with Distributional Risk-averse formulation

Pierre Clavier, Stephanie Allassonniere, Erwan Le Pennec

Keywords: Robust Reinforcement Learning, Risk-Averse Reinforcement Learning, Deep Reinforcement Learning

Abstract: The purpose of robust reinforcement learning is to make predictions more robust to changes in the dynamics or rewards of the system. This problem is particularly important when dynamics and rewards of the environment are estimated from the data. However, without constraints, this problem is intractable. In this paper, we approximate the Robust Reinforcement Learning constrained with a $f$-divergence using an approximate Risk-Averse formulation. We show that the classical Reinforcement Learning formulation can be robustified using a standard deviation penalization of the objective. Two algorithms based on Distributional Reinforcement Learning, one for discrete and one for continuous action spaces, are proposed and tested in a classical Gym environment to demonstrate the robustness of the algorithms.

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