Inverse reinforcement learning (IRL) is the problem of inferring the reward function of an
agent, given its policy or observed behavior. Analogous to RL, IRL is perceived both as a …

Imitation learning algorithms can be used to learn a policy from expert demonstrations
without access to a reward signal. However, most existing approaches are not applicable in …

In this article, we study the problem of multiple stochastic agents interacting in a dynamic
game scenario with continuous state and action spaces. We define a new notion of …

In active visual tracking, it is notoriously difficult when distracting objects appear, as
distractors often mislead the tracker by occluding the target or bringing a confusing …

The recent mean field game (MFG) formalism has enabled the application of inverse
reinforcement learning (IRL) methods in large-scale multi-agent systems, with the goal of …

In multi-agent systems, complex interacting behaviors arise due to the high correlations
among agents. However, previous work on modeling multi-agent interactions from …

This paper concerns imitation learning (IL) in cooperative multi-agent systems. The learning
problem under consideration poses several challenges, characterized by high-dimensional …

Human-robot interactions (HRI) can be modeled as differential games with incomplete
information, where each agent holds private reward parameters. Due to the open challenge …

This paper concerns imitation learning (IL)(ie, the problem of learning to mimic expert
behaviors from demonstrations) in cooperative multi-agent systems. The learning problem …

Mean field games (MFG) facilitate the otherwise intractable reinforcement learning (RL) in
large-scale multi-agent systems (MAS), through reducing interplays among agents to those …