New Yokai Learning Environment challenges AI cooperation benchmarks

Researchers have introduced the Yokai Learning Environment (YLE), a new benchmark designed to assess multi-agent reinforcement learning algorithms in cooperative AI scenarios. Unlike its predecessor, the Hanabi Learning Environment (HLE), YLE requires agents to track and update beliefs about moving cards and infer shared knowledge to achieve effective collaboration. Initial evaluations show that leading ZSC methods, which perform exceptionally well in HLE, struggle in YLE, exhibiting persistent performance gaps and weaker belief representations. This suggests that progress measured solely on HLE may not generalize to more complex cooperative tasks. AI

IMPACT Establishes a more challenging benchmark for multi-agent cooperation, potentially driving progress in AI's ability to collaborate with unknown partners.

RANK_REASON New benchmark paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Constantin Ruhdorfer
• Hanabi Learning Environment
• High-Entropy IPPO
• Multi-Agent RL
• Off-Belief Learning
• Yokai Learning Environment
• zero-shot coordination

• paper
• other

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