The Next Input updates The Next Input-updates
Browse every published The Next Input update in a calm card overview with images, dates, and direct access to each article. Bekijk alle gepubliceerde The Next Input-updates in een rustig kaartenoverzicht met beelden, datums en directe toegang tot elk artikel.
The Next Input update The Next Input-update
OpenAI standardizes on PyTorch OpenAI standardizes on PyTorch
We are standardizing OpenAI’s deep learning framework on PyTorch. We are standardizing OpenAI’s deep learning framework on PyTorch.
The Next Input update The Next Input-update
Scaling laws for neural language models Scaling laws for neural language models
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Dota 2 with large scale deep reinforcement learning Dota 2 with large scale deep reinforcement learning
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Deep double descent Deep double descent
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Procgen Benchmark Procgen Benchmark
Procgen Benchmark consists of 16 unique environments designed to measure both sample efficiency and generalization in reinforcement learning. This benchmark is ideal for evaluating generalization since distinct training and test sets can be generated in each environment. This benchmark is also well-suited to evaluate sample efficiency, since all environments pose diverse and compelling challenges for RL agents. The environments’ intrinsic diversity demands that agents learn robust policies; overfitting to narrow regions in state space will not suffice. Put differently, the ability to generalize becomes an integral component of success when agents are faced with ever-changing levels. Procgen Benchmark consists of 16 unique environments designed to measure both sample efficiency and generalization in reinforcement learning. This benchmark is ideal for evaluating generalization since distinct training and test sets can be generated in each environment. This benchmark is also well-suited to evaluate sample efficiency, since all environments pose diverse and compelling challenges for RL agents. The environments’ intrinsic diversity demands that agents learn robust policies; overfitting to narrow regions in state space will not suffice. Put differently, the ability to generalize becomes an integral component of success when agents are faced with ever-changing levels.
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Safety Gym Safety Gym
To study constrained RL for safe exploration, we developed a new set of environments and tools called Safety Gym. By comparison to existing environments for constrained RL, Safety Gym environments are richer and feature a wider range of difficulty and complexity. To study constrained RL for safe exploration, we developed a new set of environments and tools called Safety Gym. By comparison to existing environments for constrained RL, Safety Gym environments are richer and feature a wider range of difficulty and complexity.
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Benchmarking safe exploration in deep reinforcement learning Benchmarking safe exploration in deep reinforcement learning
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GPT-2: 1.5B release GPT-2: 1.5B release
Title: GPT-2: 1.5B release Title: GPT-2: 1.5B release
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Solving Rubik’s Cube with a robot hand Solving Rubik’s Cube with a robot hand
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OpenAI Scholars 2020: Applications open OpenAI Scholars 2020: Applications open
We are now accepting applications for our third class of OpenAI Scholars. We are now accepting applications for our third class of OpenAI Scholars.
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Fine-tuning GPT-2 from human preferences Fine-tuning GPT-2 from human preferences
Title: Fine-tuning GPT-2 from human preferences Title: Fine-tuning GPT-2 from human preferences
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Emergent tool use from multi-agent interaction Emergent tool use from multi-agent interaction
Title: Emergent tool use from multi-agent interaction Title: Emergent tool use from multi-agent interaction
Showing 817 to 828 of 994 updates. Je bekijkt 817 tot 828 van 994 updates.