Learning Task-relevant Representations for Generalization via Characteristic Functions of Reward Sequence Distributions-Reference-Cited by-同舟云学术

Learning Task-relevant Representations for Generalization via Characteristic Functions of Reward Sequence Distributions

Published:2022-08-14 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
language:
Short-container-title:

Author:

Yang Rui¹,Wang Jie²,Geng Zijie¹,Ye Mingxuan¹,Ji Shuiwang³,Li Bin¹,Wu Feng¹

Affiliation:

1. University of Science and Technology of China, Hefei, China

2. Institute of Artificial Intelligence & University of Science and Technology of China, Hefei, China

3. Texas A&M University, College Station, TX, USA

Funder

University of Science and Technology of China

National Nature Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3534678.3539391

Reference35 articles.

1. Rishabh Agarwal , Marlos C. Machado , Pablo Samuel Castro , and Marc G. Bellemare . 2021 . Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning. In ICLR 2021 . Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, and Marc G. Bellemare. 2021. Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning. In ICLR 2021.

2. Abdul Fatir Ansari , Jonathan Scarlett , and Harold Soh . 2020 . A Characteristic Function Approach to Deep Implicit Generative Modeling . In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7476--7484 . Abdul Fatir Ansari, Jonathan Scarlett, and Harold Soh. 2020. A Characteristic Function Approach to Deep Implicit Generative Modeling. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7476--7484.

3. Pablo Samuel Castro . 2020 . Scalable Methods for Computing State Similarity in Deterministic Markov Decision Processes. In The Thirty-Fourth AAAI Conference on Artificial Intelligence. 10069--10076 . Pablo Samuel Castro. 2020. Scalable Methods for Computing State Similarity in Deterministic Markov Decision Processes. In The Thirty-Fourth AAAI Conference on Artificial Intelligence. 10069--10076.

4. Simon S. Du , Akshay Krishnamurthy , Nan Jiang , Alekh Agarwal , Miroslav Dudík , and John Langford . 2019 . Provably efficient RL with Rich Observations via Latent State Decoding . In ICML 2019, Vol. 97 . 1665--1674. Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, and John Langford. 2019. Provably efficient RL with Rich Observations via Latent State Decoding. In ICML 2019, Vol. 97. 1665--1674.

5. IMPALA;Espeholt Lasse;Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures. In ICML,2018

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Sequential action-induced invariant representation for reinforcement learning;Neural Networks;2024-11

2. Learning task-relevant representations via rewards and real actions for reinforcement learning;Knowledge-Based Systems;2024-06

3. HiMacMic: Hierarchical Multi-Agent Deep Reinforcement Learning with Dynamic Asynchronous Macro Strategy;Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2023-08-04