Gotta Learn Fast: A new benchmark for generalization in RL
Gotta Learn Fast: A new benchmark for generalization in RL.
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Gotta Learn Fast: A new benchmark for generalization in RL.
We’re launching a transfer learning contest that measures a reinforcement learning algorithm’s ability to generalize from previous experience.
Variance reduction for policy gradient with action-dependent factorized baselines.
On March 3rd, we hosted our first hackathon with 100 members of the artificial intelligence community.
Improving GANs using optimal transport.
On first-order meta-learning algorithms.
We’ve developed a simple meta-learning algorithm called Reptile which works by repeatedly sampling a task, performing stochastic gradient descent on it, and updating the initial parameters towards...
We’re providing 6–10 stipends and mentorship to individuals from underrepresented groups to study deep learning full-time for 3 months and open-source a project.
Some considerations on learning to explore via meta-reinforcement learning.
We’re releasing eight simulated robotics environments and a Baselines implementation of Hindsight Experience Replay, all developed for our research over the past year. We’ve used these...
Multi-Goal Reinforcement Learning: Challenging robotics environments and request for research.
Come to OpenAI’s office in San Francisco’s Mission District for talks and a hackathon on Saturday, March 3rd.
We’ve co-authored a paper that forecasts how malicious actors could misuse AI technology, and potential ways we can prevent and mitigate these threats. This paper is the outcome of almost a year...
We’ve designed a method that encourages AIs to teach each other with examples that also make sense to humans.
We’ve built a system for automatically figuring out which object is meant by a word by having a neural network decide if the word belongs to each of about 100 automatically-discovered “types”...
We’re releasing a new batch of seven unsolved problems which have come up in the course of our research at OpenAI.
We’re releasing highly-optimized GPU kernels for an underexplored class of neural network architectures: networks with block-sparse weights. Depending on the chosen sparsity, these kernels can...
Learning sparse neural networks through L₀ regularization.
Interpretable and pedagogical examples.
We’ve developed a hierarchical reinforcement learning algorithm that learns high-level actions useful for solving a range of tasks, allowing fast solving of tasks requiring thousands of timesteps....
Our latest robotics techniques allow robot controllers, trained entirely in simulation and deployed on physical robots, to react to unplanned changes in the environment as they solve simple tasks....
Sim-to-real transfer of robotic control with dynamics randomization.
Asymmetric actor critic for image-based robot learning.
Domain randomization and generative models for robotic grasping.
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...
We show that for the task of simulated robot wrestling, a meta-learning agent can learn to quickly defeat a stronger non-meta-learning agent, and also show that the meta-learning agent can adapt...
Nonlinear computation in deep linear networks.
We’re releasing an algorithm which accounts for the fact that other agents are learning too, and discovers self-interested yet collaborative strategies like tit-for-tat in the iterated prisoner’s...