William Woof's repositories

Neural-Episodic-Control

Implementation of Deepmind's Neural Episodic Control

json2vec

Implementation of Semantic Tree-structured Learning Algorithm (STRLA) for JSON data, aka. JSON Neural Network

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litematic-viewer

Viewer for Litematica schematics in-browser for when you cba to fire up Minecraft. Based on Deepslate renderer.

nn_q_learning_tensorflow

Neural network based Q-learning in tensorflow

segment-anything-webdemo

A very basic flask app showing how to use segment-anything in browser

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gym_vgdl

Port of py-vgdl to OpenAi gym

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Set-CGAN

Adaptation of conventional GAN to condition on additional input set.

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Object-Based-RL

Accompanying code for "Learning to Play General Video-Games via an Object Embedding Network"

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EndingCredits.github.io

Build a Jekyll blog in minutes, without touching the command line.

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gym-utils

A collection of wrappers and utils for OpenAI Gym (may eventually get turned int PR fro main gym repo)

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CarPC

Various files and resources for setting up a Pi based In-Car Entertainment system.. Mainly for my own reference but may be a good resource for others looking to do similar things.

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FCN.tensorflow

Tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation (http://fcn.berkeleyvision.org)

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gvgai

This is the framework for the General Video Game Competition - http://www.gvgai.net/

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gym

A toolkit for developing and comparing reinforcement learning algorithms.

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htm-tensorflow

An implementation of Numenta's HTM algorithm in Tensorflow.

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ml-agents

Unity Machine Learning Agents Toolkit

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namedtensor

Named Tensor implementation for Torch

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PedestrianRL

Reinforcement learning for pedestrian modeeling

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popclusterdemo

A demo of uses of heirarchical clustering as aid for visualisation of spatial data

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PyGame-Learning-Environment

PyGame Learning Environment (PLE) -- Reinforcement Learning Environment in Python.

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roguelike-engine

Example of basic roguelike engine in python - WIP

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segment-anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

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Sensor_Nodes

Code for the sensor nodes.

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