9 papers
Towards a Practical Understanding of Lagrangian Methods in Safe Reinforcement Learning
Lindsay Spoor, Ãlvaro Serra-Gómez, Aske Plaat +1
Safe reinforcement learning addresses constrained optimization problems where maximizing performance must be balanced against safety constraints, and Lagrangian methods are a widel…
A Unified Framework for Zero-Shot Reinforcement Learning
Jacopo Di Ventura, Jan Felix Kleuker, Aske Plaat +1
Zero-shot reinforcement learning (RL) has emerged as a setting for developing general agents, capable of solving downstream tasks without additional training or planning at test-ti…
Guiding Skill Discovery with Foundation Models
Zhao Yang, Thomas M. Moerland, Mike Preuss +3
Learning diverse skills without hand-crafted reward functions could accelerate reinforcement learning in downstream tasks. However, existing skill discovery methods focus solely on…
On the Effect of Regularization in Policy Mirror Descent
Jan Felix Kleuker, Aske Plaat, Thomas Moerland
Policy Mirror Descent (PMD) has emerged as a unifying framework in reinforcement learning (RL) by linking policy gradient methods with a first-order optimization method known as mi…
Chargax: A JAX Accelerated EV Charging Simulator
Koen Ponse, Jan Felix Kleuker, Aske Plaat +1
Deep Reinforcement Learning can play a key role in addressing sustainable energy challenges. For instance, many grid systems are heavily congested, highlighting the urgent need to…
EconoJax: A Fast & Scalable Economic Simulation in Jax
Koen Ponse, Aske Plaat, Niki van Stein +1
Accurate economic simulations often require many experimental runs, particularly when combined with reinforcement learning. Unfortunately, training reinforcement learning agents in…