Research map / Multi-agent systems and games / Cooperative multi-agent RL
Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use neighbor communication and consensus-based critic updates instead of independent local learning | Fully decentralized actor-critic · Cooperative Navigation | globally averaged return | Helped |
| Use Algorithm 2 rather than Algorithm 1 | Fully decentralized actor-critic with neural networks · Cooperative Navigation | globally averaged return | Helped |
| Use decentralized agents with neighbor communication rather than a centralized controller | Actor-critic algorithms · Cooperative Navigation | globally averaged return | No clear effect |
| Use linear function approximation | Algorithms 1 and 2 · Networked multi-agent MDP | convergence | Helped |
| Use neural networks rather than linear function approximation | Algorithms 1 and 2 · Cooperative Navigation | globally averaged return | Helped |
Closest papers by meaning
- Finite-Time Convergence and Sample Complexity of Multi-Agent Actor-Critic Reinforcement Learning with Average Reward (ICLR 2022 · similarity 0.93)
- Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning (NeurIPS 2019 · similarity 0.93)
- Multi-Agent Reinforcement Learning in Stochastic Networked Systems (NeurIPS 2021 · similarity 0.92)
- Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning (NeurIPS 2021 · similarity 0.91)
- Finite-Time Global Optimality Convergence in Deep Neural Actor-Critic Methods for Decentralized Multi-Agent Reinforcement Learning (ICML 2025 · similarity 0.91)
- Bayesian Ego-graph inference for Networked Multi-Agent Reinforcement Learning (NeurIPS 2025 · similarity 0.89)
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