  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.
