cherouss's starred repositories
rlstructures
RLStructures is a library to facilitate the implementation of new reinforcement learning algorithms. It includes a library, a tutorial, and different RL algorithms provided as examples.
RL-Adventure
Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL
intelligent_traffic_lights
Traffic Lights Control with Deep Learning
alive-progress
A new kind of Progress Bar, with real-time throughput, ETA, and very cool animations!
higgsfield
Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters
docker-sumo
Containerised SUMO. Use sumo, sumo-gui and TraCI with Docker. :whale: :car:
Adaptive-Traffic-Signal-Control-Using-Reinforcement-Learning
This is an application exploiting principles of Deep Reinforcement Learning. The Deep Neural Network is trained to approximate the Bellman Equation (Q-Learning).
svoice
We provide a PyTorch implementation of the paper Voice Separation with an Unknown Number of Multiple Speakers In which, we present a new method for separating a mixed audio sequence, in which multiple voices speak simultaneously. The new method employs gated neural networks that are trained to separate the voices at multiple processing steps, while maintaining the speaker in each output channel fixed. A different model is trained for every number of possible speakers, and the model with the largest number of speakers is employed to select the actual number of speakers in a given sample. Our method greatly outperforms the current state of the art, which, as we show, is not competitive for more than two speakers.
RL-on-SUMO
Demos of reinforcement learning on Simulation of Urban MObility
TransportationNetworks
Transportation Networks for Research
Traffic-Optimisation
Traffic Signal timings using Deep Q-Learning
dockerfiles
Various Dockerfiles I use on the desktop and on servers.
developer-handbook
An opinionated guide on how to become a professional Web/Mobile App Developer.
sampleproject
A sample project that exists for PyPUG's "Tutorial on Packaging and Distributing Projects"
tokenizers
💥 Fast State-of-the-Art Tokenizers optimized for Research and Production
design-resources-for-developers
Curated list of design and UI resources from stock photos, web templates, CSS frameworks, UI libraries, tools and much more
rnn_lstm_from_scratch
How to build RNNs and LSTMs from scratch with NumPy.
public-conventions
In-house conventions and styles