Reliant on too many experiments to learn good actions, current Reinforcement Learning (RL) algorithms have limited applicability in real-world settings, which can be too expensive to allow exploration. We propose an algorithm for batch RL, where effective policies are learned using only a fixed offline dataset instead of online interactions with the environment. The limited data in batch RL produces inherent uncertainty in value estimates of states/actions that were insufficiently represented in the training data. This leads to particularly severe extrapolation when our candidate policies diverge from one that generated the data. We propose to mitigate this issue via two straightforward penalties: a policy-constraint to reduce this divergence and a value-constraint that discourages overly optimistic estimates. Over a comprehensive set of 32 continuous-action batch RL benchmarks, our approach compares favorably to state-of-the-art methods, regardless of how the offline data were collected.

本研究提出基于批次强化学习的算法，仅使用固定的离线数据集而非在线与环境的交互来学习有效策略，并通过策略约束和价值约束对数据集不足的情况进行干扰，实现对候选策略的控制，相比于现有的最新方法在多项连续动作批处理强化学习基准测试中表现优异。

连续双重约束批次强化学习