- 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
- “Generating and Adapting to Diverse Ad-Hoc Cooperation Agents in Hanabi”, Canaan et al 2020
- “Evaluating the Rainbow DQN Agent in Hanabi With Unseen Partners”, 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
- Sort By Magic
- Wikipedia
- Miscellaneous
- Link 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
“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”
“Evaluating the Rainbow DQN Agent in Hanabi With Unseen Partners”, Canaan et al 2020
“Evaluating the Rainbow DQN Agent in Hanabi with Unseen Partners”
“"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”
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multiagent-rl
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hanabi-ai
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Wikipedia
Miscellaneous
Link Bibliography
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https://arxiv.org/abs/2311.10090: “JaxMARL: Multi-Agent RL Environments in JAX”,