基于隐式双向课程的无示范自主强化学习
Demonstration-free Autonomous Reinforcement Learning via Implicit and Bidirectional Curriculum
AI总结：
针对强化学习依赖回合重置、现有自主强化学习依赖先验数据且难应对稀疏交互的问题，提出基于隐式双向课程的无示范算法，通过条件激活辅助智能体与最优传输双向目标课程，性能超越含示范的现有方法。
摘要：
尽管强化学习（RL）仅通过与环境的交互在习得复杂技能方面已取得巨大成功，但它假设每个回合结束时都能轻松重置到初始状态。这一假设阻碍了具身智能体的自主学习，因为在物理世界中进行重置既耗时又繁琐。因此，人们对能够从非回合式交互中学习的自主强化学习（ARL）方法的兴趣日益浓厚。然而，现有的ARL研究受限于对先验数据的依赖，无法在任务相关交互稀疏的环境中学习。相比之下，我们提出了一种基于隐式双向课程（IBC）的无示范ARL算法。借助一个根据学习进度有条件激活的辅助智能体，以及基于最优传输的双向目标课程，我们的方法性能优于以往方法，甚至优于那些利用示范数据的方法。
英文摘要：
While reinforcement learning (RL) has achieved great success in acquiring complex skills solely from environmental interactions, it assumes that resets to the initial state are readily available at the end of each episode. Such an assumption hinders the autonomous learning of embodied agents due to the time-consuming and cumbersome workarounds for resetting in the physical world. Hence, there has been a growing interest in autonomous RL (ARL) methods that are capable of learning from non-episodic interactions. However, existing works on ARL are limited by their reliance on prior data and are unable to learn in environments where task-relevant interactions are sparse. In contrast, we propose a demonstration-free ARL algorithm via Implicit and Bi-directional Curriculum (IBC). With an auxiliary agent that is conditionally activated upon learning progress and a bidirectional goal curriculum based on optimal transport, our method outperforms previous methods, even the ones that leverage demonstrations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
