Research map / Multi-agent systems and games / Games and equilibria
Bi-Level Actor-Critic for Multi-Agent Coordination
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Bi-AC to learn the Stackelberg equilibrium instead of the baseline MARL methods | Escape · Escape | optimality rate: 56% -> 90% | Helped |
| Use Bi-AC to learn the Stackelberg equilibrium instead of the baseline MARL methods | Maintain · Maintain | optimality rate: 0% -> 100% | Helped |
| Use Bi-AC with the main-lane car as leader and auxiliary-lane car as follower | Highway merge · Traffic Merge | main-lane car goes first rate: 58.2% -> 72.2% | Helped |
Closest papers by meaning
- Stackelberg Actor-Critic: Game-Theoretic Reinforcement Learning Algorithms (AAAI 2022 · similarity 0.92)
- Communication-Efficient Actor-Critic Methods for Homogeneous Markov Games (ICLR 2022 · similarity 0.90)
- A Natural Actor-Critic Framework for Zero-Sum Markov Games (ICML 2022 · similarity 0.89)
- Learning in Stackelberg Mean Field Games: A Non-Asymptotic Analysis (NeurIPS 2025 · similarity 0.89)
- Sequential Asynchronous Action Coordination in Multi-Agent Systems: A Stackelberg Decision Transformer Approach (ICML 2024 · similarity 0.88)
- Maximum Entropy Heterogeneous-Agent Reinforcement Learning (ICLR 2024 · similarity 0.87)
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