7 papers
Learning More from Less: Reinforcement Learning from Hindsight
Iris Xu, Sunshine Jiang, John Marangola +8
Reinforcement learning (RL) is increasingly used to post-train vision-language-action (VLA) models, but every update consumes robot rollouts that are slow and costly to collect, ma…
Prompt-Driven Exploration
Sunshine Jiang, John Marangola, David Zhang +6
Exploration is essential to RL since a policy cannot improve by repeatedly sampling the behaviors it already prefers. Standard methods inject stochasticity in the action space, but…
Large Pre-Training Datasets Don't Always Guarantee Robustness after Fine-Tuning
Jaedong Hwang, Brian Cheung, Zhang-Wei Hong +3
Large-scale pretrained models are widely leveraged as foundations for learning new specialized tasks via fine-tuning, with the goal of maintaining the general performance of the mo…
Random Latent Exploration for Deep Reinforcement Learning
Srinath Mahankali, Zhang-Wei Hong, Ayush Sekhari +2
We introduce Random Latent Exploration (RLE), a simple yet effective exploration strategy in reinforcement learning (RL). On average, RLE outperforms noise-based methods, which per…
ORSO: Accelerating Reward Design via Online Reward Selection and Policy Optimization
Chen Bo Calvin Zhang, Zhang-Wei Hong, Aldo Pacchiano +1
Reward shaping is critical in reinforcement learning (RL), particularly for complex tasks where sparse rewards can hinder learning. However, choosing effective shaping rewards from…
Grid Cell-Inspired Fragmentation and Recall for Efficient Map Building
Jaedong Hwang, Zhang-Wei Hong, Eric Chen +3
Animals and robots navigate through environments by building and refining maps of space. These maps enable functions including navigation back to home, planning, search and foragin…