Agents
Multi-Agent Reinforcement Learning is A Sequence Modeling Problem
Our goal is to build the bridge between MARL and SMs so that the modeling power of modern sequence models can be unleashed for MARL.
Multi-Agent Reinforcement Learning is A Sequence Modeling Problem
Our goal is to build the bridge between MARL and SMs so that the modeling power of modern sequence models can be unleashed for MARL.
Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance
Individuals often make di ff erent decisions when faced with the same context, due to personal preferences and background. For instance, judges may vary in their leniency towards certain drug-related o ff enses, and doctors may vary in their preference for how to start treatment for certain types of patients.
812214fb8e7066bfa6e32c626c2c688b-Paper.pdf
In this work, we argue that the order of play in strategic classification is fundamentally determined by the relative frequencies at which the decision-maker and the agents adapt to each other's actions.
Learning to Ground Multi-Agent Communication with Autoencoders
Communication requires having a common language, a lingua franca, between agents.
