12 papers
Safe Exploration via Policy Priors
Manuel Wendl, Yarden As, Manish Prajapat +3
Safe exploration is a key requirement for reinforcement learning (RL) agents to learn and adapt online, beyond controlled (e.g. simulated) environments. In this work, we tackle thi…
Sampling-Based Safe Reinforcement Learning
Luca Vignola, Bruce D. Lee, Manish Prajapat +4
Safe exploration remains a fundamental challenge in reinforcement learning (RL), limiting the deployment of RL agents in the real world. We propose Sampling-Based Safe Reinforcemen…
When Does Non-Uniform Replay Matter in Reinforcement Learning?
Michal Korniak, MikoÅaj Czarnecki, Yarden As +3
Modern off-policy reinforcement learning algorithms often rely on simple uniform replay sampling and it remains unclear when and why non-uniform replay improves over this strong ba…
Symmetry-Guided Memory Augmentation for Efficient Locomotion Learning
Kaixi Bao, Chenhao Li, Yarden As +2
Training reinforcement learning (RL) policies for legged locomotion often requires extensive environment interactions, which are costly and time-consuming. We propose Symmetry-Guid…
What Matters for Simulation to Online Reinforcement Learning on Real Robots
Yarden As, Dhruva Tirumala, René Zurbrügg +4
We investigate what specific design choices enable successful online reinforcement learning (RL) on physical robots. Across 100 real-world training runs on three distinct robotic p…
SPiDR: A Simple Approach for Zero-Shot Safety in Sim-to-Real Transfer
Yarden As, Chengrui Qu, Benjamin Unger +6
Deploying reinforcement learning (RL) safely in the real world is challenging, as policies trained in simulators must face the inevitable sim-to-real gap. Robust safe RL techniques…