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Found 7 papers, 3 papers with code
Towards a Unifying Model of Rationality in Multiagent Systems
Multiagent systems deployed in the real world need to cooperate with other agents (including humans) nearly as effectively as these agents cooperate with one another.
Uncoupled Learning of Differential Stackelberg Equilibria with Commitments
A natural solution concept for many multiagent settings is the Stackelberg equilibrium, under which a ``leader'' agent selects a strategy that maximizes its own payoff assuming the ``follower'' chooses their best response to this strategy.
Differentiable User Models
Probabilistic user modeling is essential for building machine learning systems in the ubiquitous cases with humans in the loop.
Distributed Influence-Augmented Local Simulators for Parallel MARL in Large Networked Systems
Due to its high sample complexity, simulation is, as of today, critical for the successful application of reinforcement learning.
Best-Response Bayesian Reinforcement Learning with Bayes-adaptive POMDPs for Centaurs
Centaurs are half-human, half-AI decision-makers where the AI's goal is to complement the human.
Interactive AI with a Theory of Mind
Understanding each other is the key to success in collaboration.
Machine Teaching of Active Sequential Learners
We formulate this sequential teaching problem, which current techniques in machine teaching do not address, as a Markov decision process, with the dynamics nesting a model of the learner and the actions being the teacher's responses.
