Evo-Memory is a new streaming benchmark and evaluation framework for self-evolving memory in LLM agents, unifying over ten memory modules and introducing the ReMem pipeline for continual improvement on multi-turn and reasoning datasets.
Pommerman: A multi- agent playground
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
CL-MARL uses an adaptive curriculum scheduler called FlexDiff and Counterfactual Group Relative Policy Advantage to break static-difficulty training in MARL and achieve higher win rates on hard StarCraft maps.
Arena introduces a modular Interface design that extends OpenAI Gym wrappers to support complex multi-agent RL scenarios including self-play and cooperative-competitive interactions.
citing papers explorer
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Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory
Evo-Memory is a new streaming benchmark and evaluation framework for self-evolving memory in LLM agents, unifying over ten memory modules and introducing the ReMem pipeline for continual improvement on multi-turn and reasoning datasets.
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Overcoming Environmental Meta-Stationarity in MARL via Adaptive Curriculum and Counterfactual Group Advantage
CL-MARL uses an adaptive curriculum scheduler called FlexDiff and Counterfactual Group Relative Policy Advantage to break static-difficulty training in MARL and achieve higher win rates on hard StarCraft maps.
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Arena: a toolkit for Multi-Agent Reinforcement Learning
Arena introduces a modular Interface design that extends OpenAI Gym wrappers to support complex multi-agent RL scenarios including self-play and cooperative-competitive interactions.