Contextual Bandit Learning With Reward Oracles and Sampling Guidance in Multi-Agent Environments
Learning action policy for autonomous agents in a decentralized multi-agent environment has remained an interesting but difficult research problem. We propose to model this problem in a contextual bandit setting with delayed reward signals, in particular an individual short-term reward signal and a...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9474507/ |