We explore the problem of imitation learning (IL) in the context of
mean...
Multi-agent reinforcement learning (MARL) addresses sequential
decision-...
Constrained Markov Decision Processes (CMDPs) are one of the common ways...
Policy Optimization (PO) algorithms have been proven particularly suited...
Online learning, in the mistake bound model, is one of the most fundamen...
Inverse Reinforcement Learning (IRL) is a powerful paradigm for inferrin...
Inverse Reinforcement Learning addresses the problem of inferring an exp...
Many learning problems involve multiple agents optimizing different
inte...
In this paper we propose a data augmentation method for time series with...