DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이강복 | - |
dc.date.accessioned | 2024-05-10T16:37:28Z | - |
dc.date.available | 2024-05-10T16:37:28Z | - |
dc.date.issued | 2024 | - |
dc.identifier.other | OAK-2015-10420 | - |
dc.identifier.uri | http://postech.dcollection.net/common/orgView/200000734373 | ko_KR |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/123372 | - |
dc.description | Master | - |
dc.description.abstract | Hierarchical goal-based reinforcement learning (HGRL) is a promising approach to learning a long-horizon task by decomposing it into a series of subtasks of achieving subgoals in a shorter horizon. However, the performance of HGRL crucially depends on the design of intrinsic rewards for these subtasks: as frequently observed in prac- tice, short-sighted reward designs often lead the agent into undesirable states where the final goal is no longer achievable. One potential remedy to the issue is to provide the agent with a means to evaluate the achievability of the final goal upon the com- pletion of the subtask; yet, evaluating this achievability over a long planning horizon is a challenging task by itself. In this work, we propose a subtask reward scheme to bridge the gap between the long-horizon primary goal and short-horizon subtasks by incorporating look-ahead information towards the next subgoals. We provide an extensive empirical analysis in MuJoCo environments, demonstrating the importance of looking ahead to the subsequent sub-goals and improving the proposed framework applied to the existing HGRL baselines. | - |
dc.language | eng | - |
dc.publisher | 포항공과대학교 | - |
dc.title | Bridging Sub-Tasks to Long-Horizon Task in Hierarchical Goal-Based Reinforcement Learning | - |
dc.title.alternative | 계층적 목표기반 강화학습에서 장기적인 주요 과제와 단기적인 하위 과제 사이를 연결하기 위한 연구 | - |
dc.type | Thesis | - |
dc.contributor.college | 인공지능대학원 | - |
dc.date.degree | 2024- 2 | - |
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