Historical milestone · 2017
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Reviewed through September 18, 2026
2017 · Historical milestone
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
- Era
- 2010s
- Theme
- Multi-agent coordination
- Evidence form
- Algorithm + simulation
- School / paradigm
- Multi-agent reinforcement learning
- Institution / context
- OpenAI; UC Berkeley; McGill University
- Researchers
- Ryan Lowe; collaborators
Understand
Plain-language record, transferred from the reviewed source module.
Theory or experimental setup. Trained decentralized actors with centralized critics in mixed cooperative and competitive particle environments.
Result / historical claim. Addressed non-stationarity created by simultaneously learning peers and enabled coordinated policies in tested simulations.
Apply
Professional implication, only where the reviewed record states one.
The checked-in record does not state a separate professional application for this entry. The topic page places it in the wider research lineage: .
Verify
Evidence status, stated limitations, and the external sources this record actually carries.
Evidence form. Algorithm + simulation
Limitation / debate. Small simulated populations and centralized training inputs limited scale and external validity.
Source status. This milestone row does not carry a primary-source URL in the approved export, and we do not have a verified link for it in our own research. We do not guess one.
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