Category
Research
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Scaling laws for neural language models
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Dota 2 with large scale deep reinforcement learning
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Deep double descent
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Procgen Benchmark
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GPT-2: 1.5B release
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Solving Rubik’s Cube with a robot hand
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Emergent tool use from multi-agent interaction
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GPT-2: 6-month follow-up
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MuseNet
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Generative modeling with sparse transformers
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OpenAI Five defeats Dota 2 world champions
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Implicit generation and generalization methods for energy-based models
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Neural MMO: A massively multiagent game environment
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Better language models and their implications
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Better language models and their implications
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Computational limitations in robust classification and win-win results
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How AI training scales
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Quantifying generalization in reinforcement learning
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Spinning Up in Deep RL
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Learning concepts with energy functions
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Plan online, learn offline: Efficient learning and exploration via model-based control
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Reinforcement learning with prediction-based rewards
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FFJORD: Free-form continuous dynamics for scalable reversible generative models
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FFJORD: Free-form continuous dynamics for scalable reversible generative models
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The International 2018: Results
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Large-scale study of curiosity-driven learning
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OpenAI Five Benchmark: Results
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Learning dexterity
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Variational option discovery algorithms
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Glow: Better reversible generative models
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Learning Montezuma’s Revenge from a single demonstration
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OpenAI Five
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Retro Contest: Results
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Learning policy representations in multiagent systems
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GamePad: A learning environment for theorem proving
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Gym Retro
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AI and compute
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Evolved Policy Gradients
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Gotta Learn Fast: A new benchmark for generalization in RL
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Retro Contest
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Variance reduction for policy gradient with action-dependent factorized baselines
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Improving GANs using optimal transport
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On first-order meta-learning algorithms
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Reptile: A scalable meta-learning algorithm
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Some considerations on learning to explore via meta-reinforcement learning
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Multi-Goal Reinforcement Learning: Challenging robotics environments and request for research
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Ingredients for robotics research
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Interpretable machine learning through teaching
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Interpretable machine learning through teaching
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Discovering types for entity disambiguation
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Requests for Research 2.0
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Scaling Kubernetes to 2,500 nodes
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Block-sparse GPU kernels
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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 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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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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PixelCNN++: Improving the PixelCNN with discretized logistic mixture likelihood and other modifications
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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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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