John Schulman
Procgen Benchmark
Retro Contest: Results
OpenAI Fellows Fall 2018
Gym Retro
Retro Contest
We're launching a transfer learning contest that measures a reinforcement learning algorithm's ability to generalize from previous experience.
Reptile: A Scalable Meta-Learning Algorithm
Requests for Research 2.0
Learning a Hierarchy
OpenAI Baselines: ACKTR & A2C
We're releasing two new OpenAI Baselines implementations: ACKTR and A2C. A2C is a synchronous, deterministic variant of Asynchronous Advantage Actor Critic (A3C) which we've found gives equal performance.
Proximal Policy Optimization
We’re releasing a new class of reinforcement learning algorithms, Proximal Policy Optimization (PPO), which perform comparably or better than state-of-the-art approaches while being much simpler to implement and tune.
OpenAI Baselines: DQN
We're open-sourcing OpenAI Baselines, our internal effort to reproduce reinforcement learning algorithms with performance on par with published results. We'll release the algorithms over upcoming months; today's release includes DQN and three of its variants.
Roboschool
Evolution Strategies as a Scalable Alternative to Reinforcement Learning
We've discovered that evolution strategies (ES), an optimization technique that's been known for decades, rivals the performance of standard reinforcement learning (RL) techniques on modern RL benchmarks, while overcoming many of RL's inconveniences.
Generative Models
This post describes four projects that share a common theme of enhancing or using generative models, a branch of unsupervised learning techniques in machine learning.