Multiagent teams have been shown to be effective in many domains that require coordination among team members. However, finding valuable joint-actions becomes increasingly difficult in tightly-coupled domains where each agent’s performance depends on the actions of many other agents. Reward shaping partially addresses this challenge by deriving more “tuned" rewards to provide agents with additional feedback, but this approach still relies on agents ran- domly discovering suitable joint-actions. In this work, we introduce Counterfactual Agent Suggestions (CAS) as a method for injecting knowledge into an agent’s learning process within the confines of existing reward structures. We show that CAS enables agent teams to converge towards desired behaviors more reliably. We also show that improvement in team performance in the presence of suggestions extends to large teams and tightly-coupled domains.
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On the Equality of Modal Damping Power and the Average Rate of Transient Energy Dissipation in a Multimachine Power System
- Award ID(s):
- 1739206
- PAR ID:
- 10356470
- Date Published:
- Journal Name:
- IEEE Control Systems Letters
- Volume:
- 6
- ISSN:
- 2475-1456
- Page Range / eLocation ID:
- 1531 to 1536
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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