We study an approach to offline reinforcement learning (RL) based on optimally solving finitely-represented MDPs derived from a static dataset of experience. This approach can be applied on top of any learned representation and has the potential to easily support multiple solution objectives as well as zero-shot adjustment to changing environments and goals. Our main contribution is to introduce the Deep Averagers with Costs MDP (DAC-MDP) and to investigate its solutions for offline RL. DAC-MDPs are a non-parametric model that can leverage deep representations and account for limited data by introducing costs for exploiting under-represented parts of the model. In theory, we show conditions that allow for lower-bounding the performance of DAC-MDP solutions. We also investigate the empirical behavior in a number of environments, including those with image-based observations. Overall, the experiments demonstrate that the framework can work in practice and scale to large complex offline RL problems.
View on arXiv@article{shrestha2025_2010.08891,
title={ DeepAveragers: Offline Reinforcement Learning by Solving Derived Non-Parametric MDPs },
author={ Aayam Shrestha and Stefan Lee and Prasad Tadepalli and Alan Fern },
journal={arXiv preprint arXiv:2010.08891},
year={ 2025 }
}