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A Best-of-list of Robot Simulators, re-generated weekly on Wednesdays
Benchmarking Knowledge Transfer in Lifelong Robot Learning
Django Log Inspector offers real-time monitoring and analysis of log files in Django projects.
An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)
A collection of high-quality models for the MuJoCo physics engine, curated by Google DeepMind.
A multi-backend implementation of the Keras API, with support for TensorFlow, JAX, and PyTorch.
Collect Lots of Shadowsocks, ShadowsocksR, Trojan, Vmess from Public Sources & Filter Best Nodes By Speed
Implementation of clean architecture in golang with gin-gonic & gorm
A boilerplate for gin + gorm + postgres + docker
This module analyze the output of "Binance Trader Bot" and evaluate the performance of the bot.
In this notebook I used Tesla (TSLA) closing price as input of LSTM model to predict future prices.
An App for Automating Trading on Binance from a ML/DL Model.
A jupyter notebook for predicting future prices of stocks using ARIMA model.
Google Drive Public File Downloader when Curl/Wget Fails
Recognize 2000+ faces on your Jetson Nano with database auto-fill and anti-spoofing
Author implementation of the LEADER algorithm: integrating learning and planning to have safer agents
A library of reinforcement learning components and agents
Official implementation of "Cycle-Consistent Counterfactuals by Latent Transformations"
Convert Machine Learning Code Between Frameworks
Source code for the differential saliency method used in "Re-understanding Finite-State Representations of Recurrent Policy Networks"
Anomalous versions of OpenAI Gym and PyBullet3 environments
An alternative privacy-friendly YouTube frontend which is efficient by design.
Implementation of the Recurrent Implicit Quantile Networks (RIQNs), used as a baseline in the OOD detection in the anomalous RL benchmark
Code for "Dream and Search to Control: Latent Space Planning for Continuous Control"
Minimal implementation of multi-agent reinforcement learning algorithms