Category
Openai IA
Toute l’actualité IA d’Openai
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Learning sparse neural networks through L₀ regularization
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Interpretable and pedagogical examples
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Learning a hierarchy
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Generalizing from simulation
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Sim-to-real transfer of robotic control with dynamics randomization
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Asymmetric actor critic for image-based robot learning
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Domain randomization and generative models for robotic grasping
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Meta-learning for wrestling
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Competitive self-play
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Nonlinear computation in deep linear networks
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Learning to model other minds
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Learning with opponent-learning awareness
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Learning with opponent-learning awareness
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OpenAI Baselines: ACKTR & A2C
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More on Dota 2
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Dota 2
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Gathering human feedback
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Better exploration with parameter noise
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Proximal Policy Optimization
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Robust adversarial inputs
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Hindsight Experience Replay
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Teacher–student curriculum learning
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Faster physics in Python
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Learning from human preferences
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Learning to cooperate, compete, and communicate
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UCB exploration via Q-ensembles
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OpenAI Baselines: DQN
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Robots that learn
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Roboschool
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Equivalence between policy gradients and soft Q-learning
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Stochastic Neural Networks for hierarchical reinforcement learning
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Stochastic Neural Networks for hierarchical reinforcement learning
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Unsupervised sentiment neuron
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Unsupervised sentiment neuron
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Spam detection in the physical world
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Spam detection in the physical world
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Evolution strategies as a scalable alternative to reinforcement learning
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One-shot imitation learning
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Distill
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Learning to communicate
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Emergence of grounded compositional language in multi-agent populations
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Prediction and control with temporal segment models
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Third-person imitation learning
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Attacking machine learning with adversarial examples
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Adversarial attacks on neural network policies
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Team update
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PixelCNN++: Improving the PixelCNN with discretized logistic mixture likelihood and other modifications
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Faulty reward functions in the wild
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Universe
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#Exploration: A study of count-based exploration for deep reinforcement learning
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#Exploration: A study of count-based exploration for deep reinforcement learning
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OpenAI and Microsoft
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On the quantitative analysis of decoder-based generative models
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A connection between generative adversarial networks, inverse reinforcement learning, and energy-based models
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RL²: Fast reinforcement learning via slow reinforcement learning
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Variational lossy autoencoder
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Extensions and limitations of the neural GPU
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Semi-supervised knowledge transfer for deep learning from private training data
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Report from the self-organizing conference
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Transfer from simulation to real world through learning deep inverse dynamics model
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Infrastructure for deep learning
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Machine Learning Unconference
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Team update
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Special projects
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Concrete AI safety problems
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OpenAI technical goals
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Generative models
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Team update
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Adversarial training methods for semi-supervised text classification
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OpenAI Gym Beta
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Welcome, Pieter and Shivon!
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Team++
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Weight normalization: A simple reparameterization to accelerate training of deep neural networks
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Introducing OpenAI