Multi-agent Reinforcement Learning in Sequential Social Dilemmas.¶
Leibo, J. Z., Zambaldi, V., Lanctot, M., Marecki, J., & Graepel, T. (2017). Multi-agent Reinforcement Learning in Sequential Social Dilemmas. Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 464-473.
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- In multi-agent reinforcement learning, a designer who wants cooperative rather than competitive convergence writes each agent's reward as its own task reward plus a weighted term in other agents' rewards; the sign and size of that cross-argument weight determine whether agents learn to share, ignore, or sabotage one another, and tuning it is the direct engineering analogue of the economist's "target the cross-argument weights" move.
This sourceShows reward shaping with cross-agent terms steers convergence toward cooperative versus competitive equilibria.
- In multi-agent reinforcement learning, a designer who wants cooperative rather than competitive convergence writes each agent's reward as its own task reward plus a weighted term in other agents' rewards; the sign and size of that cross-argument weight determine whether agents learn to share, ignore, or sabotage one another, and tuning it is the direct engineering analogue of the economist's "target the cross-argument weights" move.
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