## Publications

**Improved Sample Complexity Bounds For Distributionally Robust Reinforcement Learning**

__Zaiyan Xu__*, Kishan Panaganti*, Dileep Kalathil

International Conference on Artificial Intelligence and Statistics (AISTATS), 2023

[abstract][arXiv][code][poster][cite]

We consider the problem of learning a control policy that is robust against the parameter mismatches between the training environment and testing environment. We formulate this as a distributionally robust reinforcement learning (DR-RL) problem where the objective is to learn the policy which maximizes the value function against the worst possible stochastic model of the environment in an uncertainty set. We focus on the tabular episodic learning setting where the algorithm has access to a generative model of the nominal (training) environment around which the uncertainty set is defined. We propose the Robust Phased Value Learning (RPVL) algorithm to solve this problem for the uncertainty sets specified by four different divergences: total variation, chi-square, Kullback-Leibler, and Wasserstein. We show that our algorithm achieves \(\tilde{\mathcal{O}}(|\mathcal{S}||\mathcal{A}| H^{5})\) sample complexity, which is uniformly better than the existing results by a factor of \(|\mathcal{S}|\), where \(|\mathcal{S}|\) is number of states, \(|\mathcal{A}|\) is the number of actions, and \(H\) is the horizon length. We also provide the first-ever sample complexity result for the Wasserstein uncertainty set. Finally, we demonstrate the performance of our algorithm using simulation experiments.

**Robust Reinforcement Learning Using Offline Data**

*Kishan Panaganti, *__Zaiyan Xu__, Dileep Kalathil, Mohammad Ghavamzadeh

Neural Information Processing Systems (NeurIPS), 2022

[abstract][arXiv][code][cite]

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. We prove that RFQI learns a near-optimal robust policy under standard assumptions and demonstrate its superior performance on standard benchmark problems.

(* denotes equal contribution)