Asynchronous Method for Deep Reinforcement Learning
-
Updated
Oct 18, 2017 - Python
Asynchronous Method for Deep Reinforcement Learning
Implementation of something related to Neural Episodic Control in Tensorflow.
A TensorFlow implementation of DeepMind's WaveNet paper
RocAlphaGo with date of death of Samatha Smith in Python 3.x
Implementation based on DeepMind's paper: Neural Episodic Control using tensorflow.
An implementation of DeepMind's Deep-Q-Network agent to play the notorious FlappyBird game.
Delusions in sequence models for interaction and control
This project implements an AI that learns the Snake game through Deep Q-Learning. It uses Fast Forward and CNN-based training to learn the optimal game strategy and visualises the learning process.
Tensorflow implementation of the paper "Neural Arithmetic Logic Units"
A set of experiments and human-playing comparisons with the Muzero agent from Google DeepMind, made as part of a research project with l'école polytechnique.
PyTorch Implementation of Relation Network on Sort-of-CLEVR Dataset
I’ll be testing different Gemma models and sharing the results here and on my Hugging Face space. Stay tuned for updates!
Unofficial code for the deep reinforcement model Agent57.
Farama Gymnasium API Wrapper for the DeepMind Control Suite and DeepMind Robot Manipulation Tasks
✭ MAGNETRON TECHNOLOGY ™ ✭: AlphaStar has the same kind of constraints that humans play under – including viewing the world through a camera, which makes it ideal to adapt to ARTIFICIAL INTELLIGENCE 2.0 ™ for DRONEBOT ™ and QUEEN ™ of PHALANX ™/HIVE ™/SWARM ™ (see ARTIFICIAL INTELLIGENCE 2.0 ™ Documentation). AlphaStar is the first AI to reach t…
MLP-Mixer: An all-MLP Architecture for Vision
A deep-q reinforcement learning agent built from the ground up in Python to play Atari 2600 games based on the work published by DeepMind.
Add a description, image, and links to the deepmind topic page so that developers can more easily learn about it.
To associate your repository with the deepmind topic, visit your repo's landing page and select "manage topics."