As robots and other intelligent agents move from simple environments and problems to more
complex, unstructured settings, manually programming their behavior has become …

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 …

Learning from demonstration, or imitation learning, is the process of learning to act in an
environment from examples provided by a teacher. Inverse reinforcement learning (IRL) is a …

The current reward learning from human preferences could be used to resolve complex
reinforcement learning (RL) tasks without access to a reward function by defining a single …

The problem of inverse reinforcement learning (IRL) is relevant to a variety of tasks including
value alignment and robot learning from demonstration. Despite significant algorithmic …

This paper develops a distributed approach for inverse optimal control (IOC) in multi-agent
systems. Here each agent can only communicate with certain nearby neighbors and only …

This paper presents a method for inverse learning of a control objective defined in terms of
requirements and their joint probability distribution from data. The probability distribution …

Artificial agents that interact with other (human or artificial) agents require models in order to
reason about those other agents' behavior. In addition to the predictive utility of these …

Inverse optimal control can be used to characterize behavior in sequential decision-making
tasks. Most existing work, however, is limited to fully observable or linear systems, or …

We synthesize shared control protocols subject to probabilistic temporal logic specifications.
Specifically, we develop a framework in which a human and an autonomy protocol can issue …