Excited to share our paper “Palette Inpainting Diffusion Curriculum Reinforcement Learning (PIDCRL)”, accepted at WCCI 2026! Curriculum Reinforcement Learning (CRL) helps agents learn faster by structuring tasks from easy → hard. We introduce PIDCRL — a diffusion-based CRL framework that converts trajectory heatmaps into candidate goals via a pretrained image-to-image diffusion model. To select meaningful goals, we employ several strategies,…

Accepted at IEEE WCCI 2026, our work "Fuzzy Logic Theory-based Adaptive Reward Shaping for Robust Reinforcement Learning (FARS)" explores how fuzzy-logic-based reward shaping can improve reinforcement learning in complex, real-world scenarios with high-dimensional states and long horizons.

How do we stabilize learning in differentiable simulation? Our new work, accepted at IEEE WCCI 2026, “ARE: Adaptive TD-λ Return Estimation for Learning Control in Differentiable Simulation,” proposes to tackle this challenge by replacing traditional k-step returns with TD-λ returns in first-order model-based reinforcement learning (FO-MBRL) algorithms.