Papers Game of Hanabi
“Game of Hanabi” 태그가 달린 논문 5편 · 필터 해제
Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay Challenge In the Game of Hanabi
Cooperative Multi-agent Reinforcement Learning (MARL) algorithms with Zero-Shot Coordination (ZSC) have gained significant attention in recent years. ZSC refers to the ability of agents to coordinate zero-shot (without a…
Game of HanabiMulti-agent Reinforcement LearningQ-LearningOn-the-fly Strategy Adaptation for ad-hoc Agent Coordination
Training agents in cooperative settings offers the promise of AI agents able to interact effectively with humans (and other agents) in the real world. Multi-agent reinforcement learning (MARL) has the potential to achiev…
Game of HanabiMulti-agent Reinforcement LearningImproving Policies via Search in Cooperative Partially Observable Games
Recent superhuman results in games have largely been achieved in a variety of zero-sum settings, such as Go and Poker, in which agents need to compete against others. However, just like humans, real-world AI systems have…
Game of HanabiThe Hanabi Challenge: A New Frontier for AI Research
From the early days of computing, games have been important testbeds for studying how well machines can do sophisticated decision making. In recent years, machine learning has made dramatic advances with artificial agent…
Decision MakingGame of HanabiEvaluating and Modelling Hanabi-Playing Agents
Agent modelling involves considering how other agents will behave, in order to influence your own actions. In this paper, we explore the use of agent modelling in the hidden-information, collaborative card game Hanabi. W…
Game of Hanabi