This post provides a comprehensive overview of RL and graph mining methods and generalize these methods to Graph Reinforcement Learning (GRL) as a unified formulation.

This post introduces Primal Wasserstein Imitation Learning (PWIL) method, based on Wasserstein distance, derives a reward function offline and efficiently replicates expert behavior in MuJoCo continuous control tasks.

This post introduces an algorithm for rewriting in materialization-based OWL 2 RL systems, guaranteeing correctness, improving efficiency, and enabling effective parallelization, resulting in orders of magnitude reduction in reasoning times on practical datasets.

This post introduces an algorithm for rewriting in materialization-based OWL 2 RL systems, guaranteeing correctness, improving efficiency, and enabling effective parallelization, resulting in orders of magnitude reduction in reasoning times on practical datasets.

This post introduces competitive programming with Rust and provides useful resources.