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NVIDIA Research: Transferring Dexterous Manipulation from GPU Simulation to a Remote, Real-World, TriFinger Task

Large-scale GPU-based simulation enables robot learning in simulation, and can be transferred to real robots without the need for physical access to the robots. 12 MIN READ
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Discovering GPU-friendly Deep Neural Networks with Unified Neural Architecture Search

After the first successes of deep learning, designing neural network architectures with desirable performance criteria for a given task (for example… 9 MIN READ
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Enhancing Sample Efficiency in Reinforcement Learning with Nonparametric Methods

Recent developments in artificial intelligence and autonomous learning have shown impressive results in tasks like board games and computer games. However… 10 MIN READ
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Powering AutoML-enabled AI Model Training with Clara Train

Deep neural networks (DNNs) have been successfully applied to volume segmentation and other medical imaging tasks. They are capable of achieving state-of-the… 8 MIN READ
Figure 1: Value iteration constructs the value function over all states over time. Here each square is a state: S is the start state, G the goal state, T squares are traps, and black squares cannot be entered. In value iteration we initialize the rewards (traps and goal state) and then these reward values spread over time until an equilibrium is reached. Depending on the penalty value on traps and the reward value for the goal different solution patterns might emerge; the last two grids show such solution states.
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Deep Learning in a Nutshell: Reinforcement Learning

This post is Part 4 of the Deep Learning in a Nutshell series, in which I’ll dive into reinforcement learning, a type of machine learning in which agents take… 28 MIN READ
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Train Your Reinforcement Learning Agents at the OpenAI Gym

Today OpenAI, a non-profit artificial intelligence research company, launched OpenAI Gym, a toolkit for developing and comparing reinforcement learning… 9 MIN READ