635 citations · 1.4k across the 6 of their papers we have counts for
8 papers
Exploration in Deep Reinforcement Learning: A Survey
Pawel Ladosz, Lilian Weng, Minwoo Kim +1
This paper reviews exploration techniques in deep reinforcement learning. Exploration techniques are of primary importance when solving sparse reward problems. In sparse reward pro…
Text and Code Embeddings by Contrastive Pre-Training
Arvind Neelakantan, Tao Xu, Raul Puri +22
Text embeddings are useful features in many applications such as semantic search and computing text similarity. Previous work typically trains models customized for different use c…
Asymmetric self-play for automatic goal discovery in robotic manipulation
OpenAI OpenAI, Matthias Plappert, Raul Sampedro +13
We train a single, goal-conditioned policy that can solve many robotic manipulation tasks, including tasks with previously unseen goals and objects. We rely on asymmetric self-play…
Automatic Curriculum Learning For Deep RL: A Short Survey
Rémy Portelas, Cédric Colas, Lilian Weng +2
Automatic Curriculum Learning (ACL) has become a cornerstone of recent successes in Deep Reinforcement Learning (DRL).These methods shape the learning trajectories of agents by cha…
Solving Rubik's Cube with a Robot Hand
OpenAI, Ilge Akkaya, Marcin Andrychowicz +16
We demonstrate that models trained only in simulation can be used to solve a manipulation problem of unprecedented complexity on a real robot. This is made possible by two key comp…
ORRB -- OpenAI Remote Rendering Backend
Maciek Chociej, Peter Welinder, Lilian Weng
We present the OpenAI Remote Rendering Backend (ORRB), a system that allows fast and customizable rendering of robotics environments. It is based on the Unity3d game engine and int…