no code implementations • 18 Mar 2024 • Hanxi Wan, Pei Li, Arpan Kusari
With the advent of universal function approximators in the domain of reinforcement learning, the number of practical applications leveraging deep reinforcement learning (DRL) has exploded.
no code implementations • 15 Sep 2022 • Arpan Kusari, Wenbo Sun
Subgraph isomorphism or subgraph matching is generally considered as an NP-complete problem, made more complex in practical applications where the edge weights take real values and are subject to measurement noise and possible anomalies.
no code implementations • 28 Jul 2022 • Pei Li, Huizhong Guo, Shan Bao, Arpan Kusari
To evaluate pedestrian safety proactively, surrogate safety measures (SSMs) have been widely used in traffic conflict-based studies as they do not require historical crashes as inputs.
1 code implementation • 23 May 2022 • Arpan Kusari, Wenbo Sun
A major challenge in LiDAR data analysis arises from the irregular nature of LiDAR data that forces practitioners to either regularize the data using some form of gridding or utilize a triangular mesh such as triangulated irregular network (TIN).
no code implementations • 1 Dec 2020 • Arpan Kusari
Current deep reinforcement learning (DRL) algorithms utilize randomness in simulation environments to assume complete coverage in the state space.
no code implementations • 2 Oct 2019 • Arpan Kusari
Inverse reinforcement learning (IRL) is used to infer the reward function from the actions of an expert running a Markov Decision Process (MDP).
1 code implementation • 11 Sep 2019 • Arpan Kusari, Jonathan P. How
A Gaussian process is used to obtain a smooth interpolation over the reward function weights of the optimal value function for three well-known examples: GridWorld, Objectworld and Pendulum.