Turnover-Orthogonal Credit Assignment for Open-Team Multi-Agent Reinforcement Learning
Open-team multi-agent reinforcement learning studies cooperative systems in which agents may join, leave, or be replaced during an episode. In such settings, the team return changes both because agents choose useful actions and because the active population itself changes. Standard centralized critics and shared advantages often mix these two effects into one scalar credit signal, allowing surviving agents to be rewarded or penalized for exogenous turnover events outside their control. We introduce turnover-orthogonal credit assignment (TOCA), a value decomposition for open teams that separates action effects, pure turnover effects, and action--turnover interactions. Under exogenous turno...
| Authors | Amit Thakur, Mukesh Singhal |
|---|---|
| Primary category | cs.MA |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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