Preference-based opponent shaping in differentiable games

arXiv:2412.03072 · cs.AI · Submitted 2024-12-04 · Read on arXiv

cs.AI

Submitted: 2024-12-04

Updated: 2026-09-25

Terminology

• Loss Function — 43× in this paper · explained in 2 episodes
• Cooperation — 24× in this paper · explained in 2 episodes
• Reinforcement Learning — 12× in this paper · explained in 4 episodes
• Preference-based Opponent Shaping — 7× in this paper
• Generalization — 4× in this paper · explained in 13 episodes
• Competitive Gradient Descent — 2× in this paper
• Objective Function — 2× in this paper · explained in 2 episodes
• Actor — 1× in this paper · explained in 3 episodes
• Adaptability — 1× in this paper · explained in 1 episode
• Constant-preference-based Opponent Shaping — 1× in this paper
• Core — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Dynamic Modeling — 1× in this paper · explained in 3 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Learning with Opponent Learning Awareness — 1× in this paper
• Multi-Agent Reinforcement Learning (MARL) — 1× in this paper · explained in 4 episodes
• Multi-Agent System — 1× in this paper · explained in 5 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Non-stationarity — 1× in this paper · explained in 2 episodes
• Q-learning — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Sample Complexity — 1× in this paper · explained in 15 episodes
• Stable Opponent Shaping — 1× in this paper

Sources

• Reasoning about Hypothetical Agent Behaviours and their Parameters
• Learning with Opponent-Learning Awareness
• Analysing the Sample Complexity of Opponent Shaping
• Stable Opponent Shaping in Differentiable Games
• Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization
• Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning
• Game-Theoretic Multiagent Reinforcement Learning

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