Search Results for author:
Found 4 papers, 1 papers with code
Random Actions vs Random Policies: Bootstrapping Model-Based Direct Policy Search
This paper studies the impact of the initial data gathering method on the subsequent learning of a dynamics model.
E2R: a Hierarchical-Learning inspired Novelty-Search method to generate diverse repertoires of grasping trajectories
Robotics grasping refers to the task of making a robotic system pick an object by applying forces and torques on its surface.
Automatic Acquisition of a Repertoire of Diverse Grasping Trajectories through Behavior Shaping and Novelty Search
Grasping a particular object may require a dedicated grasping movement that may also be specific to the robot end-effector.
Exploratory State Representation Learning
Our experimental results show that the approach leads to efficient exploration in challenging environments with image observations, and to state representations that significantly accelerate learning in RL tasks.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
