Which Experiences Are Influential for RL Agents? Efficiently Estimating The Influence of Experiences
원문 초록
In reinforcement learning (RL) with experience replay, experiences stored in a replay buffer influence the RL agent's performance. Information about how these experiences influence the agent's performance is valuable for various purposes, such as identifying experiences that negatively influence underperforming agents. One method for estimating the influence of experiences is the leave-one-out (LOO) method. However, this method is usually computationally prohibitive. In this paper, we present Policy Iteration with Turn-over Dropout (PIToD), which efficiently estimates the influence of experiences. We evaluate how accurately PIToD estimates the influence of experiences and its efficiency compared to LOO. We then apply PIToD to amend underperforming RL agents, i.e., we use PIToD to estimate negatively influential experiences for the RL agents and to delete the influence of these experiences. We show that RL agents' performance is significantly improved via amendments with PIToD.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Fully Offline Reinforcement Learning
High-Throughput Distributed Reinforcement Learning via Adaptive Policy Synchronization
Personalized Exercise Recommendation with Semantically-Grounded Knowledge Tracing
Bridging the Gap in Vision Language Models in Identifying Unsafe Concepts Across Modalities
Exploring the robustness of TractOracle methods in RL-based tractography
Reasoning or Memorization? Unreliable Results of Reinforcement Learning Due to Data Contamination
토론
이 논문과 연결된 포럼 글이 아직 없습니다.