论文标题
探索在任务转移中的作用在加强学习中
The Role of Exploration for Task Transfer in Reinforcement Learning
论文作者
论文摘要
探索 - 在加强学习中探索折衷（RL）是一个众所周知且备受瞩目的问题，它平衡了贪婪的行动选择与新经验的研究，并且通常仅在学习单个学习任务的最佳政策中考虑探索方法的研究。但是，在在线任务转移的背景下，在线操作期间任务发生了变化，我们假设预计需要适应未来任务的勘探策略可能会对转移效率产生明显的影响。因此，我们重新检查了探索 - 在转移学习的背景下探索了权衡。在这项工作中，我们审查了强化学习探索方法，定义了一个分类法，可以在任务转移的背景下分析这些方法的差异，并为未来的研究提出了途径。
The exploration--exploitation trade-off in reinforcement learning (RL) is a well-known and much-studied problem that balances greedy action selection with novel experience, and the study of exploration methods is usually only considered in the context of learning the optimal policy for a single learning task. However, in the context of online task transfer, where there is a change to the task during online operation, we hypothesize that exploration strategies that anticipate the need to adapt to future tasks can have a pronounced impact on the efficiency of transfer. As such, we re-examine the exploration--exploitation trade-off in the context of transfer learning. In this work, we review reinforcement learning exploration methods, define a taxonomy with which to organize them, analyze these methods' differences in the context of task transfer, and suggest avenues for future investigation.
