OMDPG combines optimal marginal Q-values with pessimistic Q-critics to reconcile monotonic improvement with partial parameter sharing in heterogeneous multi-agent RL.
the Thirty-Eighth Annual Conference on Neural Information Pro- cessing Systems (NeurIPS) (2024)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.AI 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
Improving monotonic optimization in heterogeneous multi-agent reinforcement learning with optimal marginal deterministic policy gradient
OMDPG combines optimal marginal Q-values with pessimistic Q-critics to reconcile monotonic improvement with partial parameter sharing in heterogeneous multi-agent RL.