# Robust Offline Reinforcement Learning for Non-Markovian Decision Processes

arXiv: https://arxiv.org/abs/2411.07514 · HTML: https://mlsanity.com/paper/2411.07514/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Offline RL algorithms, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 35 of its 71 references that are in the map.
- First posted on arXiv 12 Nov 2024.

## Placed in

- Offline RL and imitation learning from data / [Offline RL algorithms](https://mlsanity.com/offlinerl/offline-rl-algorithms/)

## Builds on (most cited first, 30 of 35)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Conservative Q-Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2006.04779/) (2020)
- [Sim-to-Real Transfer of Robotic Control with Dynamics Randomization](https://mlsanity.com/paper/1710.06537/) (2017)
- [MOPO: Model-based Offline Policy Optimization](https://mlsanity.com/paper/2005.13239/) (2020)
- [MOReL : Model-Based Offline Reinforcement Learning](https://mlsanity.com/paper/2005.05951/) (2020)
- [Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey](https://mlsanity.com/paper/2009.13303/) (2020)
- [Information-Theoretic Considerations in Batch Reinforcement Learning](https://mlsanity.com/paper/1905.00360/) (2019)
- [Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism](https://mlsanity.com/paper/2103.12021/) (2021)
- [Bellman-consistent Pessimism for Offline Reinforcement Learning](https://mlsanity.com/paper/2106.06926/) (2021)
- [How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned](https://mlsanity.com/paper/2102.02915/) (2021)
- [Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage](https://mlsanity.com/paper/2107.06226/) (2021)
- [Mastering Complex Control in MOBA Games with Deep Reinforcement Learning](https://mlsanity.com/paper/1912.09729/) (2019)
- [Representation Learning for Online and Offline RL in Low-rank MDPs](https://mlsanity.com/paper/2110.04652/) (2021)
- [Online Robust Reinforcement Learning with Model Uncertainty](https://mlsanity.com/paper/2109.14523/) (2021)
- [Robust Reinforcement Learning using Offline Data](https://mlsanity.com/paper/2208.05129/) (2022)
- [Sample Complexity of Robust Reinforcement Learning with a Generative Model](https://mlsanity.com/paper/2112.01506/) (2021)
- [Near-Optimal Offline Reinforcement Learning via Double Variance Reduction](https://mlsanity.com/paper/2102.01748/) (2021)
- [Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game](https://mlsanity.com/paper/2205.15512/) (2022)
- [Reinforcement Learning under Model Mismatch](https://mlsanity.com/paper/1706.04711/) (2017)
- [Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage](https://mlsanity.com/paper/2305.09659/) (2023)
- [GEC: A Unified Framework for Interactive Decision Making in MDP, POMDP, and Beyond](https://mlsanity.com/paper/2211.01962/) (2022)
- [PAC Reinforcement Learning for Predictive State Representations](https://mlsanity.com/paper/2207.05738/) (2022)
- [Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs](https://mlsanity.com/paper/1902.07605/) (2019)
- [Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems](https://mlsanity.com/paper/2206.12020/) (2022)
- [Distributionally Robust Counterpart in Markov Decision Processes](https://mlsanity.com/paper/1501.07418/) (2015)
- [Partially Observable RL with B-Stability: Unified Structural Condition and Sharp Sample-Efficient Algorithms](https://mlsanity.com/paper/2209.14990/) (2022)
- [End-to-End Offline Goal-Oriented Dialog Policy Learning via Policy Gradient](https://mlsanity.com/paper/1712.02838/) (2017)
- [On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation](https://mlsanity.com/paper/2211.13208/) (2022)
- [Robust Average-Reward Markov Decision Processes](https://mlsanity.com/paper/2301.00858/) (2023)

## Abstract (arXiv)

Distributionally robust offline reinforcement learning (RL) aims to find a policy that performs the best under the worst environment within an uncertainty set using an offline dataset collected from a nominal model. While recent advances in robust RL focus on Markov decision processes (MDPs), robust non-Markovian RL is limited to planning problem where the transitions in the uncertainty set are known. In this paper, we study the learning problem of robust offline non-Markovian RL. Specifically, when the nominal model admits a low-rank structure, we propose a new algorithm, featuring a novel dataset distillation and a lower confidence bound (LCB) design for robust values under different types of the uncertainty set. We also derive new dual forms for these robust values in non-Markovian RL, making our algorithm more amenable to practical implementation. By further introducing a novel type-I concentrability coefficient tailored for offline low-rank non-Markovian decision processes, we prove that our algorithm can find an ε-optimal robust policy using O(1/ε2) offline samples. Moreover, we extend our algorithm to the case when the nominal model does not have specific structure. With a new type-II concentrability coefficient, the extended algorithm also enjoys polynomial sample efficiency under all different types of the uncertainty set.