Search Results for author:
Found 3 papers, 1 papers with code
On-Policy Policy Gradient Reinforcement Learning Without On-Policy Sampling
We empirically evaluate PROPS on both continuous-action MuJoCo benchmark tasks as well as discrete-action tasks and demonstrate that (1) PROPS decreases sampling error throughout training and (2) improves the data efficiency of on-policy policy gradient algorithms.
Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning
In offline reinforcement learning (RL), an RL agent learns to solve a task using only a fixed dataset of previously collected data.
Understanding when Dynamics-Invariant Data Augmentations Benefit Model-Free Reinforcement Learning Updates
Recently, data augmentation (DA) has emerged as a method for leveraging domain knowledge to inexpensively generate additional data in reinforcement learning (RL) tasks, often yielding substantial improvements in data efficiency.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
