l2rpn

Reinforcement Learning using the Actor-Critic framework for the L2RPN challenge (https://l2rpn.chalearn.org/ & https://competitions.codalab.org/competitions/22845#learn_the_details-overview). The agent trained using this code was one of the winners of the challenge. The code runs on the pypownet environment (https://github.com/MarvinLer/pypownet)…

The implementation of NeurIPS_2020_L2RPN_Track1(Robustness) and Track2 (Adaptability) Competition

Source code for the papers: RL for Mitigating Cascading Failures: Targeted Exploration via Sensitivity Factors (NeurIPS) / Blackout Mitigation via Physics-guided RL (IEEE TPS). Built with TensorFlow.

Research on Hierarchical Multi-Agent Reinforcement Learning for Power Grid Topology Control (L2RPN). This project explores hierarchical MARL architectures, regional agents for stabilizing power grids using the Grid2Op environment.

This project for implementing and training baseline agent such are PPO, Actor Critic, SACD, DQN variants on PowerGrid(L2RPN)

Curiosity-driven Actor–Critic using JEPA latent dynamics.

Research implementation of Meta-Learning (MAML) in Multi-Agent Reinforcement Learning (MARL) for power grid control using L2RPN. This project explores how meta-learning can improve agent adaptation across varying grid topologies and operational scenarios, aiming for safer and more efficient grid management.