Which Experiences Are Influential for RL Agents? Efficiently Estimating The Influence of Experiences
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916850386862080 |
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| author | Hiraoka, Takuya Wang, Guanquan Onishi, Takashi Tsuruoka, Yoshimasa |
| author_facet | Hiraoka, Takuya Wang, Guanquan Onishi, Takashi Tsuruoka, Yoshimasa |
| contents | 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 correctly 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_14629 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Which Experiences Are Influential for RL Agents? Efficiently Estimating The Influence of Experiences Hiraoka, Takuya Wang, Guanquan Onishi, Takashi Tsuruoka, Yoshimasa Machine Learning Artificial Intelligence 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 correctly 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. |
| title | Which Experiences Are Influential for RL Agents? Efficiently Estimating The Influence of Experiences |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2405.14629 |