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Found 11 papers, 1 papers with code
EconGym: A Scalable AI Testbed with Diverse Economic Tasks
Artificial intelligence (AI) has become a powerful tool for economic research, enabling large-scale simulation and policy optimization.
Amulet: ReAlignment During Test Time for Personalized Preference Adaptation of LLMs
How to align large language models (LLMs) with user preferences from a static general dataset has been frequently studied.
Magnetic Preference Optimization: Achieving Last-iterate Convergence for Language Model Alignment
Self-play methods have demonstrated remarkable success in enhancing model capabilities across various domains.
A Survey on Self-play Methods in Reinforcement Learning
Self-play, characterized by agents' interactions with copies or past versions of themselves, has recently gained prominence in reinforcement learning (RL).
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Fusion-PSRO: Nash Policy Fusion for Policy Space Response Oracles
For solving zero-sum games involving non-transitivity, a useful approach is to maintain a policy population to approximate the Nash Equilibrium (NE).
Learning Macroeconomic Policies through Dynamic Stackelberg Mean-Field Games
Macroeconomic outcomes emerge from individuals' decisions, making it essential to model how agents interact with macro policy via consumption, investment, and labor choices.
Roadmap on Incentive Compatibility for AI Alignment and Governance in Sociotechnical Systems
The burgeoning integration of artificial intelligence (AI) into human society brings forth significant implications for societal governance and safety.
Panacea: Pareto Alignment via Preference Adaptation for LLMs
Panacea trains a single model capable of adapting online and Pareto-optimally to diverse sets of preferences without the need for further tuning.
Evolving Diverse Red-team Language Models in Multi-round Multi-agent Games
Furthermore, we develop a Gamified Red Team Solver (GRTS) with diversity measures to mitigate mode collapse and theoretically guarantee the convergence of approximate Nash equilibrium which results in better strategies for both teams.
Scalable Model-based Policy Optimization for Decentralized Networked Systems
Reinforcement learning algorithms require a large amount of samples; this often limits their real-world applications on even simple tasks.
Game-Theoretic Multiagent Reinforcement Learning
In this work, we provide a monograph on MARL that covers both the fundamentals and the latest developments in the research frontier.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
