Research map / Multi-agent systems and games / Cooperative multi-agent RL
Episodic Multi-agent Reinforcement Learning with Curiosity-driven Exploration
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add curiosity module (individual Q-value prediction intrinsic reward) to QPLEX with episodic memory (ablation) | EMC · SMAC super hard maps | win rate | Helped |
| add curiosity module to QPLEX with episodic memory (ablation) | EMC · SMAC easy maps | win rate | No clear effect |
| too small episodic memory weight lambda (ablation) | EMC · SMAC | win rate | Hurt |
| too large episodic memory weight lambda (ablation) | EMC · SMAC super hard maps | win rate | Hurt |
| predict individual Q-values instead of observations (RND) for intrinsic reward | QPLEX · didactic grid world | test win rate | Helped |
Closest papers by meaning
- Efficient Episodic Memory Utilization of Cooperative Multi-Agent Reinforcement Learning (ICLR 2024 · similarity 0.90)
- Wonder Wins Ways: Curiosity-Driven Exploration through Multi-Agent Contextual Calibration (NeurIPS 2025 · similarity 0.89)
- Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning (AAAI 2024 · similarity 0.89)
- Enhancing Cooperative Multi-Agent Reinforcement Learning with State Modelling and Adversarial Exploration (ICML 2025 · similarity 0.88)
- Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement Learning (ICML 2024 · similarity 0.88)
- Learning Joint Behaviors with Large Variations (AAAI 2025 · similarity 0.87)
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