In offline reinforcement learning (RL), the goal is to learn a highly
rewarding policy based solely on a dataset of historical interactions with the
environment. The ability to train RL policies offline can greatly expand the
applicability of RL, its data efficiency, and its experimental velocity. Prior
work in offline RL has been confined almost exclusively to model-free RL
approaches. In this work, we present MOReL, an algorithmic framework for
model-based offline RL. This framework consists of two steps: (a) learning a
pessimistic MDP (P-MDP) using the offline dataset; and (b) learning a
near-optimal policy in this P-MDP. The learned P-MDP has the property that for
any policy, the performance in the real environment is approximately
lower-bounded by the performance in the P-MDP. This enables it to serve as a
good surrogate for purposes of policy evaluation and learning, and overcome
common pitfalls of model-based RL like model exploitation. Theoretically, we
show that MOReL is minimax optimal (up to log factors) for offline RL. Through
experiments, we show that MOReL matches or exceeds state-of-the-art results in
widely studied offline RL benchmarks. Moreover, the modular design of MOReL
enables future advances in its components (e.g. generative modeling,
uncertainty estimation, planning etc.) to directly translate into advances for
offline RL.

本研究提出了基于模型的离线强化学习算法 MOReL，具有模块化设计，可以用于模型生成、不确定性估计、规划等领域，实验结果表明，MOReL 能够达到或超过当前广泛研究的离线强化学习基准的最新结果。