An entity-graph MARL framework (RACHE) using R-GCN message passing and attention pooling over train-service nodes outperforms baseline algorithms in railway pricing revenue across two simulated market scenarios.
Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, and Shimon Whiteson
3 Pith papers cite this work. Polarity classification is still indexing.
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TRACER combines a controller-regret layer using regret matching for speak/skip decisions with a generation-credit layer using GSPO rewards to enable learned collaboration in multi-LLM reasoning.
COSAC enables scalable per-agent policy gradients in sequential cooperative teams via ridge regression on additive reward decomposition and counterfactual advantages from fictitious policy continuations, extending aristocrat utility with controlled bias-variance bounds.
citing papers explorer
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Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets
An entity-graph MARL framework (RACHE) using R-GCN message passing and attention pooling over train-service nodes outperforms baseline algorithms in railway pricing revenue across two simulated market scenarios.
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TRACER: Turn-level Regret Matching with Inner Reinforcement Credit for Cooperative Multi-LLM Reasoning
TRACER combines a controller-regret layer using regret matching for speak/skip decisions with a generation-credit layer using GSPO rewards to enable learned collaboration in multi-LLM reasoning.
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COSAC: Counterfactual Credit Assignment in Sequential Cooperative Teams
COSAC enables scalable per-agent policy gradients in sequential cooperative teams via ridge regression on additive reward decomposition and counterfactual advantages from fictitious policy continuations, extending aristocrat utility with controlled bias-variance bounds.