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Robust Reinforcement Learning using Offline Data

Series: Bangalore Theory Seminars

Speaker: Kishan Panaganti, CalTech

Date/Time: Mar 04 11:00:00

Location: CSA Seminar Hall (Room No. 254, First Floor)

Abstract:
The goal of robust reinforcement learning (RL) is to learn a policy that is robust against the uncertainty in model parameters. Parameter uncertainty commonly occurs in many real-world RL applications due to simulator modeling errors, changes in the real-world system dynamics over time, and adversarial disturbances. Robust RL is typically formulated as a max-min problem, where the objective is to learn the policy that maximizes the value against the worst possible models that lie in an uncertainty set. In this work, we propose a robust RL algorithm called Robust Fitted Q-Iteration (RFQI), which uses only an offline dataset to learn the optimal robust policy. Robust RL with offline data is significantly more challenging than its non-robust counterpart because of the minimization over all models present in the robust Bellman operator. This poses challenges in offline data collection, optimization over the models, and unbiased estimation. In this work, we propose a systematic approach to overcome these challenges, resulting in our RFQI algorithm.


Microsoft teams link:

Link


We are grateful to the Kirani family for generously supporting the theory seminar series

Hosts: Rameesh Paul, KVN Sreenivas, Rahul Madhavan, Manisha Padala