arXiv AI
Cooperating with Future Collaborators: Multi-Agent RL under Staggered Participation
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arXiv Machine Learning
Jul 22
HERAKLES: Hierarchical Skill Compilation for Open-ended LLM Agents
arXiv:2508. 14751v2 Announce Type: replace Abstract: We study goal-conditioned reinforcement learning in partially observable environments with sparse rewards and large, structured goal spaces.
arXiv AI
Jun 3
Automata-Conditioned Cooperative Multi-Agent Reinforcement Learning
arXiv:2511. 02304v2 Announce Type: replace-cross Abstract: We study learning multi-task, multi-agent policies for cooperative, temporal objectives, under centralized training, decentralized execution.
arXiv AI
Jun 16
Learning to Share: Selective Memory for Efficient Parallel Agentic Systems
arXiv:2602. 05965v2 Announce Type: replace-cross Abstract: Agentic systems solve complex tasks by coordinating multiple agents that iteratively reason, invoke tools, and exchange intermediate results.
arXiv AI
Sep 18
UnifiedPlayers: Enhance Tool-Integrated Reasoning in Agentic Reinforcement Learning
UnifiedPlayers is a cooperative framework that jointly adapts planning, execution, and evaluation for tool-integrated reinforcement learning agents. It consists of a Planning Player that generates tasks, an Execution Player that creates multi-turn trajectories with Python tool calls, and an Evaluation Player that builds executable verifiers, all coordinated by role‑specific rewards under GRPO. The approach outperforms prior baselines on mathematical and general reasoning benchmarks and yields a verifier with high adversarial detection accuracy and more discriminative reward signals.
arXiv AI
Sep 17
AeroWeaver: An Embodied-Agent Harness for Weaving Aerial Skills into Distributed, Adaptive Swarm Execution
AeroWeaver is a new embodied‑agent harness that integrates large language model (LLM) decision making with the executable skills of individual UAVs, enabling distributed, adaptive swarm execution. It connects semantic mission decisions to governed skills, organizes role‑conditioned local agents for coordination, and refines skill selection online using role‑indexed state‑action‑reward experience. Experiments demonstrate that AeroWeaver maintains valid skill execution without a central joint‑action generator and supports reward‑guided, training‑free adaptive learning from accumulated execution experience.
arXiv AI
Jun 26
Joint Learning of Experiential Rules and Policies for Large Language Model Agents
arXiv:2606. 27136v1 Announce Type: new Abstract: For LLM agents in multi-step interactive environments, a key challenge is to make effective use of accumulated interaction experience.