5 papers · 1 filter
SHAPO: Sharpness-Aware Policy Optimization for Safe Exploration
Kaustubh Mani, Yann Pequignot, Vincent Mai +1
Safe exploration is a prerequisite for deploying reinforcement learning (RL) agents in safety-critical domains. In this paper, we approach safe exploration through the lens of epis…
Safety Representations for Safer Policy Learning
Kaustubh Mani, Vincent Mai, Charlie Gauthier +3
Reinforcement learning algorithms typically necessitate extensive exploration of the state space to find optimal policies. However, in safety-critical applications, the risks assoc…
Accelerating Quasi-Static Time Series Simulations with Foundation Models
Alban Puech, François Mirallès, Jonas Weiss +5
Quasi-static time series (QSTS) simulations have great potential for evaluating the grid's ability to accommodate the large-scale integration of distributed energy resources. Howev…
Active Learning-Based Optimization of Hydroelectric Turbine Startup to Minimize Fatigue Damage
Vincent Mai, Quang Hung Pham, Arthur Favrel +2
Hydro-generating units (HGUs) play a crucial role in integrating intermittent renewable energy sources into the power grid due to their flexible operational capabilities. This evol…
Foundation Models for the Electric Power Grid
Hendrik F. Hamann, Thomas Brunschwiler, Blazhe Gjorgiev +24
Foundation models (FMs) currently dominate news headlines. They employ advanced deep learning architectures to extract structural information autonomously from vast datasets throug…