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ARXIV:2604.18190 · MULTI-AGENT RL · SUBMITTED 21 APR · 20:33 UTC · FRESHNESS STALE
ARXIV:2604.18190MULTI-AGENT RLSUBMITTED 21 APR · 20:33 UTCFRESHNESS STALETim Goppelsroeder · Rasmus Jensen · arXiv
A scalable multi-agent reinforcement learning algorithm that reduces computational cost by restricting critic attention to nearby agents.
Opportunity summary
Pain A scalable multi-agent reinforcement learning algorithm that reduces computational cost by restricting critic attention to nearby agents.
Evidence 0 refs | 4 sources | 83% coverage
Blocker Evidence partial
A scalable multi-agent reinforcement learning algorithm that reduces computational cost by restricting critic attention to nearby agents. Centralized critics, which condition on the observations and actions of all agents, have demonstrated significant performance gains…
We propose MADDPG-K, a scalable extension to Multi-Agent Deep Deterministic Policy Gradient (MADDPG) that addresses the computational limitations of centralized critic approaches. Centralized critics, which condition on the observations and actions of all agents,…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our code is available at https://github.com/TimGop/MADDPG-K . A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper…
Multi-Agent RL moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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A scalable multi-agent reinforcement learning algorithm that reduces computational cost by restricting critic attention to nearby agents.
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Paper Pack
10.48550/arXiv.2604.18190A scalable multi-agent reinforcement learning algorithm that reduces computational cost by restricting critic attention to nearby agents.
Abstract
We propose MADDPG-K, a scalable extension to Multi-Agent Deep Deterministic Policy Gradient (MADDPG) that addresses the computational limitations of centralized critic approaches. Centralized critics, which condition on the observations and actions of all agents, have demonstrated significant performance gains in cooperative and competitive multi-agent settings. However, their critic networks grow linearly in input size with the number of agents, making them increasingly expensive to train at scale. MADDPG-K mitigates this by restricting each agent's critic to the $k$ closest agents under a chosen metric which in our case is Euclidean distance. This ensures a constant-size critic input regardless of the total agent count. We analyze the complexity of this approach, showing that the quadratic cost it retains arises from cheap scalar distance computations rather than the expensive neural network matrix multiplications that bottleneck standard MADDPG. We validate our method empirically across cooperative and adversarial environments from the Multi-Particle Environment suite, demonstrating competitive or superior performance compared to MADDPG, faster convergence in cooperative settings, and better runtime scaling as the number of agents grows. Our code is available at https://github.com/TimGop/MADDPG-K .
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Proof status
partial0 refs; 4 sources; 83% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 7.0
PROBLEM
A scalable multi-agent reinforcement learning algorithm that reduces computational cost by restricting critic attention to nearby agents. Centralized critics, which condition on the observations and actions of all agents, have demonstrated significant performance gains in cooper...
METHOD
We propose MADDPG-K, a scalable extension to Multi-Agent Deep Deterministic Policy Gradient (MADDPG) that addresses the computational limitations of centralized critic approaches. Centralized critics, which condition on the observations and actions of all agents, have demonstrat...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our code is available at https://github.com/TimGop/MADDPG-K . A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
WHY NOW
Multi-Agent RL moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
{"file name": "input.pdf", "number of pages": 17, "author": "Tim Goppelsroeder; Rasmus Jensen", "title": "Scalable Neighborhood-Based Multi-Agent Actor-Critic", "creation date": null, "modification date": null
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partial
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A scalable multi-agent reinforcement learning algorithm that reduces computational cost by restricting critic attention to nearby agents.
Segment
Multi-Agent RL
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Commercial read
7.0/10 public viability
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2/3 checks · 67%
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reason
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proof status
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stale
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