‘Hanabi AI’ directory
- 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 & Allen 2022
- “Conditional Imitation Learning for Multi-Agent Games”, Shih et al 2022
- “Reinforcement Learning on Human Decision Models for Uniquely Collaborative AI Teammates”, Kantack 2021
- “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 & Foerster 2019
- “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
- “Playing Hanabi Near-Optimally”, Bouzy 2017
- “How to Make the Perfect Fireworks Display: Two Strategies for Hanabi”, Cox et al 2015
- “State-Of-The-Art Hanabi Bots + Simulation Framework in Rust”
- Sort By Magic
- Wikipedia (1)
- Bibliography
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
Human-AI Coordination via Human-Regularized Search and Learning
“Is Vanilla Policy Gradient Overlooked? Analyzing Deep Reinforcement Learning for Hanabi”, Grooten et al 2022
Is Vanilla Policy Gradient Overlooked? Analyzing Deep Reinforcement Learning for Hanabi
“On-The-Fly Strategy Adaptation for Ad-Hoc Agent Coordination”, Zand et al 2022
On-the-fly Strategy Adaptation for ad-hoc Agent Coordination
“Any-Play: An Intrinsic Augmentation for Zero-Shot Coordination”, Lucas & Allen 2022
Any-Play: An Intrinsic Augmentation for Zero-Shot Coordination
“Conditional Imitation Learning for Multi-Agent Games”, Shih et al 2022
“Reinforcement Learning on Human Decision Models for Uniquely Collaborative AI Teammates”, Kantack 2021
Reinforcement Learning on Human Decision Models for Uniquely Collaborative AI Teammates
“Scalable Online Planning via Reinforcement Learning Fine-Tuning”, Fickinger et al 2021
Scalable Online Planning via Reinforcement Learning Fine-Tuning
“Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi”, Siu et al 2021
Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi
“Learned Belief Search: Efficiently Improving Policies in Partially Observable Settings”, Hu et al 2021
Learned Belief Search: Efficiently Improving Policies in Partially Observable Settings
“On the Critical Role of Conventions in Adaptive Human-AI Collaboration”, Shih et al 2021
On the Critical Role of Conventions in Adaptive Human-AI Collaboration
“Off-Belief Learning”, Hu et al 2021
“Continuous Coordination As a Realistic Scenario for Lifelong Learning”, Nekoei et al 2021
Continuous Coordination As a Realistic Scenario for Lifelong Learning
“The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games”, Yu et al 2021
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
“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
Evaluating the Rainbow DQN Agent in Hanabi with Unseen Partners
“Generating and Adapting to Diverse Ad-Hoc Cooperation Agents in Hanabi”, Canaan et al 2020
Generating and Adapting to Diverse Ad-Hoc Cooperation Agents in Hanabi
“"Other-Play" for Zero-Shot Coordination”, Hu et al 2020
“Improving Policies via Search in Cooperative Partially Observable Games”, Lerer et al 2019
Improving Policies via Search in Cooperative Partially Observable Games
“Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning”, Hu & Foerster 2019
Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning
“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
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning
“Playing Hanabi Near-Optimally”, Bouzy 2017
“How to Make the Perfect Fireworks Display: Two Strategies for Hanabi”, Cox et al 2015
How to Make the Perfect Fireworks Display: Two Strategies for Hanabi
“State-Of-The-Art Hanabi Bots + Simulation Framework in Rust”
Sort By Magic
Annotations sorted by machine learning into inferred 'tags'. This provides an alternative way to browse: instead of by date order, one can browse in topic order. The 'sorted' list has been automatically clustered into multiple sections & auto-labeled for easier browsing.
Beginning with the newest annotation, it uses the embedding of each annotation to attempt to create a list of nearest-neighbor annotations, creating a progression of topics. For more details, see the link.
multi-agent-rl
multi-agent-cooperation
online-planning
hanabi-cooperation
Wikipedia (1)
Bibliography
https://arxiv.org/abs/2311.10090: “JaxMARL: Multi-Agent RL Environments in JAX”,https://arxiv.org/abs/2103.01955: “The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games”,