We study the problem of representational transfer in RL, where an agent first pretrains in a number of source tasks to discover a shared representation, which is subsequently used to learn a good policy in a \emph{target task}. We propose a new notion of task relatedness between source and target tasks, and develop a novel approach for representational transfer under this assumption. Concretely, we show that given generative access to source tasks, we can discover a representation, using which subsequent linear RL techniques quickly converge to a near-optimal policy in the target task. The sample complexity is close to knowing the ground truth features in the target task, and comparable to prior representation learning results in the source tasks. We complement our positive results with lower bounds without generative access, and validate our findings with empirical evaluation on rich observation MDPs that require deep exploration. In our experiments, we observe a speed up in learning in the target by pre-training, and also validate the need for generative access in source tasks.

翻译：我们研究强化学习中的表征迁移问题，其中智能体先在多个源任务中进行预训练以发现共享表征，随后利用该表征在目标任务中学习优质策略。我们提出了源任务与目标任务间任务相关性的新概念，并在此假设下开发了表征迁移的新方法。具体而言，我们证明：若能够以生成式方式访问源任务，则可发现一种表征，使得后续线性强化学习技术能快速收敛至目标任务的近乎最优策略。该方法的样本复杂度接近于已知目标任务真实特征的情况，且与先前源任务中的表征学习结果相当。我们通过无生成式访问情况下的下界结论补充了正面结果，并在需要深度探索的丰富观测马尔可夫决策过程上通过实证评估验证了研究发现。实验中发现，预训练可加速目标任务的学习过程，同时验证了源任务中生成式访问的必要性。