We've trained a human-like robot hand to manipulate physical objects with unprecedented dexterity.
Our team of five neural networks, OpenAI Five, has started to defeat amateur human teams at Dota 2.
We've found that self-play allows simulated AIs to discover physical skills like tackling, ducking, faking, kicking, catching, and diving for the ball, without explicitly designing an environment with these skills in mind.
We've found that adding adaptive noise to the parameters of reinforcement learning algorithms frequently boosts performance. This exploration method is simple to implement and very rarely decreases performance, so it's worth trying on any problem.
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.
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.
