Humans, even as infants, use cognitive strategies, such as exploration and hypothesis
testing, to learn about causal interactions in the environment. In animal learning studies …

Animal visual perception is an important technique for automatically monitoring animal
health, understanding animal behaviors, and assisting animal-related research. However, it …

Reinforcement learning (RL) models are used extensively to study human behavior. These
rely on normative models of behavior and stress interpretability over predictive capabilities …

Modeling of real-world biological multi-agents is a fundamental problem in various scientific
and engineering fields. Reinforcement learning (RL) is a powerful framework to generate …

Cellular heterogeneity is a ubiquitous aspect of biology and a major obstacle to successful
cancer treatment. Several techniques have emerged to quantify heterogeneity in live cells …

The discovery of individual objectives in collective behavior of complex dynamical systems
such as fish schools and bacteria colonies is a long-standing challenge. Inverse …

We developed two online data-driven methods for estimating an objective function in
continuous-time linear and nonlinear deterministic systems. The primary focus addressed …

Despite the recent successes of multi-agent reinforcement learning (MARL) algorithms,
efficiently adapting to co-players in mixed-motive environments remains a significant …

Inverse reinforcement Learning (IRL) has emerged as a powerful paradigm for extracting
expert skills from observed behavior, with applications ranging from autonomous systems to …

In advancing the understanding of natural decision-making processes, inverse
reinforcement learning (IRL) methods have proven instrumental in reconstructing animal's …