Pre-training with offline data and online fine-tuning using reinforcement learning is a promising strategy for learning control policies by leveraging the best of both worlds in terms of sample efficiency and performance. One natural approach is to initialize the policy for online learning with the one trained offline. In this work, we introduce a policy expansion scheme for this task. After learning the offline policy, we use it as one candidate policy in a policy set. We then expand the policy set with another policy which will be responsible for further learning. The two policies will be composed in an adaptive manner for interacting with the environment. With this approach, the policy previously learned offline is fully retained during online learning, thus mitigating the potential issues such as destroying the useful behaviors of the offline policy in the initial stage of online learning while allowing the offline policy participate in the exploration naturally in an adaptive manner. Moreover, new useful behaviors can potentially be captured by the newly added policy through learning. Experiments are conducted on a number of tasks and the results demonstrate the effectiveness of the proposed approach.

翻译：利用离线数据进行预训练，再通过强化学习进行在线微调，是一种有前景的控制策略学习策略，它结合了样本效率和性能两方面的优势。一种自然的方法是用离线训练的策略来初始化在线学习的策略。在本工作中，我们为此任务引入了一种策略扩展方案。在学习离线策略后，我们将其用作策略集合中的一个候选策略。然后，我们通过添加另一个负责进一步学习的策略来扩展策略集合。这两个策略将以自适应的方式组合起来与环境交互。通过这种方法，离线学习到的策略在在线学习过程中得以完全保留，从而缓解了诸如在线学习初期破坏离线策略有用行为等潜在问题，同时允许离线策略以自适应方式自然地参与探索。此外，新添加的策略可能通过学习捕获新的有用行为。我们在多个任务上进行了实验，结果证明了所提方法的有效性。