Search Results for author:
Found 10 papers, 5 papers with code
Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning
As a result, it is usually necessary to reproduce the experiments from scratch, which can be time-consuming and error-prone.
Adversarial Style Transfer for Robust Policy Optimization in Deep Reinforcement Learning
An additional goal of the generator is to perturb the observation, which maximizes the agent's probability of taking a different action.
Adversarial Policy Optimization in Deep Reinforcement Learning
Data augmentation can provide a performance boost to RL agents by mitigating the effect of overfitting.
Accelerating Policy Gradient by Estimating Value Function from Prior Computation in Deep Reinforcement Learning
Our approach is to estimate the value function from prior computations, such as from the Q-network learned in DQN or the value function trained for different but related environments.
Robust Policy Optimization in Deep Reinforcement Learning
We observed that in many settings, RPO increases the policy entropy early in training and then maintains a certain level of entropy throughout the training period.
Bootstrap Advantage Estimation for Policy Optimization in Reinforcement Learning
Unlike using data augmentation on the input to learn value and policy function as existing methods use, our method uses data augmentation to compute a bootstrap advantage estimation.
Bootstrap State Representation using Style Transfer for Better Generalization in Deep Reinforcement Learning
Deep Reinforcement Learning (RL) agents often overfit the training environment, leading to poor generalization performance.
Adversarial Style Transfer for Robust Policy Optimization in Reinforcement Learning
An additional goal of the generator is to perturb the observation, which maximizes the agent's probability of taking a different action.
DESK: A Robotic Activity Dataset for Dexterous Surgical Skills Transfer to Medical Robots
It comprises a set of surgical robotic skills collected during a surgical training task using three robotic platforms: the Taurus II robot, Taurus II simulated robot, and the YuMi robot.
Robotics
A Case Study on the Impact of Similarity Measure on Information Retrieval based Software Engineering Tasks
In particular, we analyze two previously proposed tools for project recommendation and bug localization tasks, which leverage diverse software artifacts, and observe that an informed choice of similarity measure indeed leads to improved performance of the existing SE tools.
