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Found 7 papers, 2 papers with code
Demystifying Deep Reinforcement Learning-Based Autonomous Vehicle Decision-Making
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.
Uncertainty-aware Efficient Subgraph Isomorphism using Graph Topology
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.
A Probabilistic Framework for Estimating the Risk of Pedestrian-Vehicle Conflicts at Intersections
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.
Graph-theoretical approach to robust 3D normal extraction of LiDAR data
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).
Assessing and Accelerating Coverage in Deep Reinforcement Learning
Current deep reinforcement learning (DRL) algorithms utilize randomness in simulation environments to assume complete coverage in the state space.
CWAE-IRL: Formulating a supervised approach to Inverse Reinforcement Learning problem
Inverse reinforcement learning (IRL) is used to infer the reward function from the actions of an expert running a Markov Decision Process (MDP).
Predicting optimal value functions by interpolating reward functions in scalarized multi-objective reinforcement learning
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.
