DEFENDER：基于动态时间规整的示范轨迹过滤方法以增强强化学习安全性
DEFENDER: DTW-Based Episode Filtering Using Demonstrations for Enhancing RL Safety
AI总结：
提出一种任务无关的强化学习安全增强方法，利用安全与不安全示范轨迹，通过动态时间规整比较进行过滤，显著降低崩溃率并保持或提升性能。
摘要：
在现实世界中部署强化学习智能体可能因试错学习带来的风险而具有挑战性。我们提出了一种任务无关的方法，利用少量安全和不安全的示范来提升强化学习智能体在学习过程中的安全性。该方法在每一步将智能体的当前轨迹与两组示范进行比较，如果轨迹与不安全示范相似，则对其进行过滤。我们对不同的过滤策略进行了消融研究，并考察了示范数量对性能的影响。我们的方法兼容任何独立的强化学习算法，并可应用于任何任务。我们在OpenAI Gym的Mujoco基准测试中的三个任务以及两个最先进的强化学习算法上评估了我们的方法。结果表明，我们的方法显著降低了智能体的崩溃率，同时收敛到独立智能体的性能，并且在大多数情况下甚至超越了其性能。
英文摘要：
Deploying reinforcement learning agents in the real world can be challenging due to the risks associated with learning through trial and error. We propose a task-agnostic method that leverages small sets of safe and unsafe demonstrations to improve the safety of RL agents during learning. The method compares the current trajectory of the agent with both sets of demonstrations at every step, and filters the trajectory if it resembles the unsafe demonstrations. We perform ablation studies on different filtering strategies and investigate the impact of the number of demonstrations on performance. Our method is compatible with any stand-alone RL algorithm and can be applied to any task. We evaluate our method on three tasks from OpenAI Gym's Mujoco benchmark and two state-of-the-art RL algorithms. The results demonstrate that our method significantly reduces the crash rate of the agent while converging to, and in most cases even improving, the performance of the stand-alone agent.
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。
