FACMAC: Factored multi−agent centralised policy gradients
We propose FACtored Multi-Agent Centralised policy gradients (FACMAC), a new method for cooperative multi-agent reinforcement learning in both discrete and continuous action spaces. Like MADDPG, a popular multi-agent actor-critic method, our approach uses deep deterministic policy gradients to learn...
| Main Authors: | , , , , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
NeurIPS
2022
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