Agents
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.
PettingZoo: A Standard API for Multi-Agent Reinforcement Learning J. K. Terry
This paper introduces the PettingZoo library and the accompanying Agent Environment Cycle ("AEC") games model. PettingZoo is a library of diverse sets of multi-agent environments with a universal, elegant Python API. PettingZoo was developed with the goal of accelerating research in Multi-Agent Reinforcement Learning ("MARL "), by making work more interchangeable, accessible and reproducible akin to what OpenAI's Gym library did for single-agent reinforcement
Sequential Information Design: Learning to Persuade in the Dark
We consider settings where the receiver faces a sequential decision making (SDM) problem.