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Tuesday, 7 June 2022

Deep Q-Learning with Space Invaders

Hugging Face 4 years ago 25

Researchers at Hugging Face present Deep Q-Learning, an extension of Q-Learning that uses neural networks to approximate Q-values instead of storing them in tables, enabling training in large state spaces like Atari games. The state space of Space Invaders is 256^100800, making a traditional Q-table with 14 states (FrozenLake) or 500 states (Taxi) impractical by comparison. The approach stabilizes training through experience replay, fixed Q-targets, and Double DQN to reduce overestimation of Q-values.

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Hugging Face Reinforcement Learning

The Annotated Diffusion Model

Hugging Face 4 years ago 19

Researchers have published an annotated guide explaining how denoising diffusion probabilistic models (DDPMs) work, breaking down the mathematical framework and PyTorch implementation for image generation. The approach trains a neural network over 1,000 time steps to gradually reverse a fixed noise-addition process, learning to predict added Gaussian noise at each step. This enables the model to generate new images by sampling random noise and iteratively denoising it, a technique now used in systems like DALL-E 2 and Latent Diffusion.

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Image Generation Diffusion Models

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