“‘Hanabi AI’ Tag”,2020-11-26 (MARL)⁠:

Bibliography for tag reinforcement-learning/imperfect-information/hanabi, most recent first: 23 annotations (parent).

• See Also
• Links
  • “JaxMARL: Multi-Agent RL Environments in JAX”, Rutherford et al 2023
  • “Human-AI Coordination via Human-Regularized Search and Learning”, Hu et al 2022
  • “Is Vanilla Policy Gradient Overlooked? Analyzing Deep Reinforcement Learning for Hanabi”, Grooten et al 2022
  • “On-The-Fly Strategy Adaptation for Ad-Hoc Agent Coordination”, Zand et al 2022
  • “Any-Play: An Intrinsic Augmentation for Zero-Shot Coordination”, Lucas & Allen2022
  • “Conditional Imitation Learning for Multi-Agent Games”, Shih et al 2022
  • “Reinforcement Learning on Human Decision Models for Uniquely Collaborative AI Teammates”, Kantack2021
  • “Scalable Online Planning via Reinforcement Learning Fine-Tuning”, Fickinger et al 2021
  • “Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi”, Siu et al 2021
  • “Learned Belief Search: Efficiently Improving Policies in Partially Observable Settings”, Hu et al 2021
  • “On the Critical Role of Conventions in Adaptive Human-AI Collaboration”, Shih et al 2021
  • “Off-Belief Learning”, Hu et al 2021
  • “Continuous Coordination As a Realistic Scenario for Lifelong Learning”, Nekoei et al 2021
  • “The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games”, Yu et al 2021
  • “Theory of Mind for Deep Reinforcement Learning in Hanabi”, Fuchs et al 2021
  • “Evaluating the Rainbow DQN Agent in Hanabi With Unseen Partners”, Canaan et al 2020
  • “Generating and Adapting to Diverse Ad-Hoc Cooperation Agents in Hanabi”, Canaan et al 2020
  • “”Other-Play” for Zero-Shot Coordination”, Hu et al 2020
  • “Improving Policies via Search in Cooperative Partially Observable Games”, Lerer et al 2019
  • “Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning”, Hu & Foerster2019
  • “The Hanabi Challenge: A New Frontier for AI Research”, Bard et al 2019
  • “Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning”, Foerster et al 2018
  • “State-Of-The-Art Hanabi Bots + Simulation Framework in Rust”
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