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- OpenAIOpenAI Baselines: DQNCompanies: OpenAIWe’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.
- OpenAIEquivalence between policy gradients and soft Q-learningCompanies: OpenAIOpenAI Abstract Two of the leading approaches for model-free reinforcement learning are policy gradient methods and Q-learning methods. Q-learning methods can be effective and sample-efficient when they work, however, it is not well-understood why they work, since empirically, the Q-values they estimate are very inaccurate. A partial explanation may be that Q-learning methods are secretly implementing policy gradient
- AdobeAdobe Bolsters eLearning Solutions With New Releases of Captivate and Captivate PrimeCompanies: AdobeAdobe today announced the latest versions of its industry-leading eLearning authoring tool, Adobe Captivate, and Learning Management System (LMS),...
- OpenAIStochastic Neural Networks for hierarchical reinforcement learningCompanies: OpenAIOpenAI Abstract Deep reinforcement learning has achieved many impressive results in recent years. However, tasks with sparse rewards or long horizons continue to pose significant challenges. To tackle these important problems, we propose a general framework that first learns useful skills in a pre-training environment, and then leverages the acquired skills for learning faster in downstream tasks. Our approach brings
- OpenAIEvolution strategies as a scalable alternative to reinforcement learningCompanies: OpenAIWe’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 (e.g. Atari/MuJoCo), while overcoming many of RL’s inconveniences.
- OpenAIOne-shot imitation learningCompanies: OpenAIAbstract Imitation learning has been commonly applied to solve different tasks in isolation. This usually requires either careful feature engineering, or a significant number of samples. This is far from what we desire: ideally, robots should be able to learn from very few demonstrations of any given task, and instantly generalize to new situations of the same task, without requiring task-specific engineering. In thi
- OpenAIDistillCompanies: OpenAIWe’re excited to support today’s launch of Distill, a new kind of journal aimed at excellent communication of machine learning results (novel or existing).
- OpenAILearning to communicateCompanies: OpenAIIn this post we’ll outline new OpenAI research in which agents develop their own language.
- OpenAIEmergence of grounded compositional language in multi-agent populationsCompanies: OpenAIAbstract By capturing statistical patterns in large corpora, machine learning has enabled significant advances in natural language processing, including in machine translation, question answering, and sentiment analysis. However, for agents to intelligently interact with humans, simply capturing the statistical patterns is insufficient. In this paper we investigate if, and how, grounded compositional language can eme
- OpenAIPrediction and control with temporal segment modelsCompanies: OpenAIAbstract We introduce a method for learning the dynamics of complex nonlinear systems based on deep generative models over temporal segments of states and actions. Unlike dynamics models that operate over individual discrete timesteps, we learn the distribution over future state trajectories conditioned on past state, past action, and planned future action trajectories, as well as a latent prior over action trajector
- OpenAIThird-person imitation learningCompanies: OpenAIAbstract Reinforcement learning (RL) makes it possible to train agents capable of achieving sophisticated goals in complex and uncertain environments. A key difficulty in reinforcement learning is specifying a reward function for the agent to optimize. Traditionally, imitation learning in RL has been used to overcome this problem. Unfortunately, hitherto imitation learning methods tend to require that demonstrations
- OpenAIAttacking machine learning with adversarial examplesCompanies: OpenAIAdversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake; they’re like optical illusions for machines. In this post we’ll show how adversarial examples work across different mediums, and will discuss why securing systems against them can be difficult.