We develop a framework to learn bio-inspired foraging policies using human data. We
conduct an experiment where humans are virtually immersed in an open field foraging …

Trust region policy optimization (TRPO) is one of the landmark policy optimization algorithms
in deep reinforcement learning. Its purpose is to maximize a surrogate objective based on …

We develop a new class of model-free deep reinforcement learning algorithms for data-
driven, learning-based control. Our Generalized Policy Improvement algorithms combine the …

Path planning is a key enabling technology for drone-based applications, where the drone
may encounter situations that require impromptu decisions to avoid task failure. Traditional …

We develop a simple framework to learn bio-inspired foraging policies using human data.
We conduct an experiment where humans are virtually immersed in an open field foraging …

Decision-focused learning (DFL) offers an end-to-end approach to the predict-then-optimize
(PO) framework by training predictive models directly on decision loss (DL), enhancing …

Deep reinforcement learning (RL) represents a data-driven framework for sequential
decision making that has demonstrated the ability to solve challenging control tasks. This …

This dissertation examines the integration of expert datasets to enhance the data efficiency
of online Deep Reinforcement Learning (DRL) algorithms in large state and action space …

We develop a method to learn bio-inspired foraging policies using human data. We conduct
an experiment where humans are virtually immersed in an open field foraging environment …

Reinforcement Learning (RL) has recently seen significant advances over the last decade in
simulated and controlled environments. RL has shown impressive results in difficult decision …