ROTATE: Regret-driven Open-ended Training for Ad Hoc Teamwork

arXiv:2505.23686 · cs.AI, cs.MA · Submitted 2025-05-29 · Read on arXiv

cs.AI, cs.MA

Submitted: 2025-05-29

Updated: 2026-09-14

Code: https://github.com/ax-ml/jax

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Regret — 141× in this paper · explained in 3 episodes
• Ad Hoc Teamwork — 17× in this paper · explained in 1 episode
• Reinforcement Learning — 17× in this paper · explained in 4 episodes
• Open-Ended Learning — 13× in this paper
• Generalization — 10× in this paper · explained in 13 episodes
• Benchmark — 9× in this paper · explained in 2 episodes
• Asymmetric Advantages — 8× in this paper
• Loss Function — 8× in this paper · explained in 2 episodes
• Counter Circuit — 7× in this paper
• Cooperation — 6× in this paper · explained in 2 episodes
• Cramped Room — 5× in this paper
• Forced Coordination — 5× in this paper
• Level-Based Foraging — 5× in this paper
• Reward Shaping — 5× in this paper · explained in 2 episodes
• Actor — 4× in this paper · explained in 3 episodes
• Mixed Play — 4× in this paper
• AI Agents — 3× in this paper · explained in 2 episodes
• Actor-Critic Architecture — 3× in this paper · explained in 1 episode
• Adam — 3× in this paper · explained in 2 episodes
• Core — 3× in this paper · explained in 2 episodes
• Generalized Advantage Estimation — 3× in this paper
• Robustness — 3× in this paper · explained in 5 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Agent Architectures — 2× in this paper · explained in 1 episode
• Deep Reinforcement Learning — 2× in this paper · explained in 2 episodes
• Reward Function — 2× in this paper · explained in 2 episodes
• Sample Efficiency — 2× in this paper · explained in 4 episodes
• Self Play — 2× in this paper
• Unified Framework — 2× in this paper · explained in 5 episodes
• Action Space — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Adaptive AI Agents — 1× in this paper · explained in 1 episode
• Adversarial Training — 1× in this paper · explained in 1 episode
• Agent Interactions — 1× in this paper · explained in 1 episode
• Catastrophic Forgetting — 1× in this paper · explained in 5 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 3 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Learning Agents Research Group — 1× in this paper
• Meta-learning — 1× in this paper · explained in 2 episodes
• Multi-Agent System — 1× in this paper · explained in 6 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Zero-Shot Transfer — 1× in this paper · explained in 4 episodes

Sources

• Improving Human-AI Coordination through Online Adversarial Training and Generative Models
• Human-compatible driving partners through data-regularized self-play reinforcement learning
• Adam: A Method for Stochastic Optimization
• High-Dimensional Continuous Control Using Generalized Advantage Estimation
• Proximal Policy Optimization Algorithms
• A Minimax Approach to Ad Hoc Teamwork
• Deep Interactive Bayesian Reinforcement Learning via Meta-Learning

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