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- OpenAIRetro ContestCompanies: OpenAIWe’re launching a transfer learning contest that measures a reinforcement learning algorithm’s ability to generalize from previous experience.
- OpenAIVariance reduction for policy gradient with action-dependent factorized baselinesCompanies: OpenAIOpenAI Abstract Policy gradient methods have enjoyed great success in deep reinforcement learning but suffer from high variance of gradient estimates. The high variance problem is particularly exasperated in problems with long horizons or high-dimensional action spaces. To mitigate this issue, we derive a bias-free action-dependent baseline for variance reduction which fully exploits the structural form of the stocha
- OpenAIReport from the OpenAI hackathonCompanies: OpenAIOn March 3rd, we hosted our first hackathon with 100 members of the artificial intelligence community.
- OpenAIImproving GANs using optimal transportCompanies: OpenAIAbstract We present Optimal Transport GAN (OT-GAN), a variant of generative adversarial nets minimizing a new metric measuring the distance between the generator distribution and the data distribution. This metric, which we call mini-batch energy distance, combines optimal transport in primal form with an energy distance defined in an adversarially learned feature space, resulting in a highly discriminative distance
- OpenAIOn first-order meta-learning algorithmsCompanies: OpenAIAbstract This paper considers meta-learning problems, where there is a distribution of tasks, and we would like to obtain an agent that performs well (i.e., learns quickly) when presented with a previously unseen task sampled from this distribution. We analyze a family of algorithms for learning a parameter initialization that can be fine-tuned quickly on a new task, using only first-order derivatives for the meta-le
- OpenAIReptile: A scalable meta-learning algorithmCompanies: OpenAIWe’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 the final parameters learned on that task. Reptile is the application of the Shortest Descent algorithm to the meta-learning setting, and is mathematically similar to first-order MAML (which is a version of the well-known MAML algorithm) that only needs black-box access to an optimizer such as SGD or Adam, with similar computational efficiency and performance.
- OpenAIOpenAI ScholarsCompanies: OpenAIWe’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.
- OpenAISome considerations on learning to explore via meta-reinforcement learningCompanies: OpenAIOpenAI Abstract We consider the problem of exploration in meta reinforcement learning. Two new meta reinforcement learning algorithms are suggested: E-MAML and E-RL². Results are presented on a novel environment we call "Krazy World" and a set of maze environments. We show E-MAML and E-RL² deliver better performance on tasks where exploration is important. Learning Paradigms Authors Bradly Stadie, Ge Yang, Rein Houth
- OpenAIIngredients for robotics researchCompanies: OpenAIWe’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 environments to train models which work on physical robots. We’re also releasing a set of requests for robotics research.
- OpenAIMulti-Goal Reinforcement Learning: Challenging robotics environments and request for researchCompanies: OpenAIOpenAI Abstract The purpose of this technical report is two-fold. First of all, it introduces a suite of challenging continuous control tasks (integrated with OpenAI Gym) based on currently existing robotics hardware. The tasks include pushing, sliding and pick & place with a Fetch robotic arm as well as in-hand object manipulation with a Shadow Dexterous Hand. All tasks have sparse binary rewards and follow a Multi-
- OpenAIOpenAI hackathonCompanies: OpenAICome to OpenAI’s office in San Francisco’s Mission District for talks and a hackathon on Saturday, March 3rd.
- OpenAIPreparing for malicious uses of AICompanies: OpenAIWe’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 of sustained work with our colleagues at the Future of Humanity Institute, the Centre for the Study of Existential Risk, the Center for a New American Security, the Electronic Frontier Foundation, and others.