In offline reinforcement learning (RL), the goal is to learn a successful policy using only a dataset of historical interactions with the environment, without any additional online interactions. This serves as an extreme test for an agent's ability to effectively use historical data, which is critical for efficient RL. 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 RL in the offline setting. This framework consists of two steps: (a) learning a pessimistic MDP model using the offline dataset; (b) learning a near-optimal policy in the learned pessimistic MDP. The construction of the pessimistic MDP is such that for any policy, the performance in the real environment is lower bounded by the performance in the pessimistic MDP. This enables the pessimistic MDP to serve as a good surrogate for the purposes of policy evaluation and learning. Overall, MOReL is amenable to detailed theoretical analysis, enables easy and transparent design of practical algorithms, and leads to state-of-the-art results on widely studied offline RL benchmark tasks.

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。

MOReL：基于模型的离线强化学习