My academic interests lie in the fields of reinforcement learning, life-long learning, bio-inspired learning algorithms and optimization applied to control problems. My current research focuses on the safe and sample-efficient training of reinforcement learning agents through the autonomous inference of domain priors, as well as other mechanisms that contribute towards the agent's domain-awareness.
News & Updates
Our paper "Learning Transferable Domain Priors for Safe Exploration in Reinforcement Learning" has been accepted at IJCNN 2020
Localization, mapping, path planning and control
Energy optimal path planning- simulation vs reality
Multiagent mapping application
EvoBot executing energy optimal navigation