Tag
Co-RL is a cooperative multi-agent framework that enables unsupervised reasoning improvement in language and vision-language models by using peer-derived rewards, reducing reliance on ground-truth labels and mitigating training collapse through increased cohort diversity.
This paper proposes a principled communication gating mechanism for multi-agent reinforcement learning using KL divergence between agents' belief distributions, showing performance improvements and interpretability on benchmarks like Predator-Prey and MPE.
The paper analyzes the safety of per-agent policy composition in multi-agent reinforcement learning, proving that independent composition can fail, and proposes MA-USFA, a hierarchical method that enables safe and flexible successor-feature transfer in cooperative MARL.
The paper proposes calibrating an artificial guilt reward signal from human neural and behavioral fMRI data, then embeds it in multi-agent PPO agents. The neurally calibrated agents match human social decision rates far better than hand-tuned or selfish baselines.
This paper introduces Inductive Logic Programming to extract symbolic representations of RL policies and proposes novel explainability metrics (activation rate, feature coverage, syntactic and semantic distance) for objective evaluation in single- and multi-agent settings.
This paper presents a cross-sectional study comparing various action factorization methods (independent networks, shared encoder, VDN, QPLEX, Joint, Auto-Regressive) across three RL algorithm families (PPO, SAC, DQN) in hybrid discrete-continuous action spaces, introducing two new lightweight environments and variants VDN-PPO and PPO-MIX.
OpenAI and University of Oxford researchers present LOLA (Learning with Opponent-Learning Awareness), a reinforcement learning method that enables agents to model and account for the learning of other agents, discovering cooperative strategies in multi-agent games like the iterated prisoner's dilemma and coin game.
OpenAI presents LOLA (Learning with Opponent-Learning Awareness), a multi-agent reinforcement learning method where agents shape the anticipated learning of other agents. The approach demonstrates emergence of cooperation in iterated prisoner's dilemma and convergence to Nash equilibrium in game-theoretic settings.