New AI agent R3D2 tackles multi-agent Hanabi with text-based learning

A research paper introduces Recurrent Replay Relevance Distributed DQN (R3D2), a novel agent designed for the Hanabi benchmark game. This agent aims to overcome limitations in traditional multi-agent reinforcement learning systems by enabling cooperation with unfamiliar collaborators and adapting to various game settings. R3D2 reformulates the task using text and employs a distributed multi-agent reinforcement learning algorithm to handle dynamic observation and action spaces, allowing it to play all game settings concurrently and transfer learned strategies to new ones. AI

IMPACT This research could advance multi-agent reinforcement learning by enabling more adaptable and cooperative AI agents in complex environments.

RANK_REASON The cluster contains a withdrawn academic paper detailing a new AI agent for a specific benchmark. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Deep Q-Network
• Hadi Nekoei
• Hanabi
• Recurrent Replay Relevance Distributed DQN

• paper
• model release

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