dc.citation.conferencePlace |
US |
- |
dc.citation.conferencePlace |
Virtual |
- |
dc.citation.title |
International Conference on Machine Learning |
- |
dc.contributor.author |
Han, Seungyul |
- |
dc.contributor.author |
Sung, Youngchul |
- |
dc.date.accessioned |
2024-01-31T21:38:20Z |
- |
dc.date.available |
2024-01-31T21:38:20Z |
- |
dc.date.created |
2021-11-28 |
- |
dc.date.issued |
2021-07-20 |
- |
dc.description.abstract |
In this paper, sample-aware policy entropy regularization is proposed to enhance the conventional policy entropy regularization for better exploration. Exploiting the sample distribution obtainable from the replay buffer, the proposed sample-aware entropy regularization maximizes the entropy of the weighted sum of the policy action distribution and the sample action distribution from the replay buffer for sample-efficient exploration. A practical algorithm named diversity actor-critic (DAC) is developed by applying policy iteration to the objective function with the proposed sample-aware entropy regularization. Numerical results show that DAC significantly outperforms existing recent algorithms for reinforcement learning. |
- |
dc.identifier.bibliographicCitation |
International Conference on Machine Learning |
- |
dc.identifier.scopusid |
2-s2.0-85129829387 |
- |
dc.identifier.uri |
https://scholarworks.unist.ac.kr/handle/201301/77155 |
- |
dc.identifier.url |
https://icml.cc/virtual/2021/spotlight/10270 |
- |
dc.publisher |
International Conference on Machine Learning |
- |
dc.title |
Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration |
- |
dc.type |
Conference Paper |
- |
dc.date.conferenceDate |
2021-07-18 |
- |