Implementation of dual deep q-learning algorithm in Python and R/Shiny to teach an agent to play the classic Atari game of Breakout.

The generalized quantile huber loss proposed in

Combining Improvements in Deep Reinforcement Learning

Master classic RL, deep RL, distributional RL, inverse RL, and more using OpenAI Gym and TensorFlow with extensive Math

Cubesat Space Protocol - A small network-layer delivery protocol designed for Cubesats

An 8b10b decoder and encoder in logic in VHDL

Create an NTP and PTP timeserver using RaspberryPi5 with BerryGPS-IMU and PPS

Protocol Buffers with small code size

Positron, a next-generation data science IDE

We provide the code repository for our paper This repository includes the necessary code to replicate our experiments and utilize our DRL model for spacecraft trajectory planning. By accessing the …

Repository with public environments for satellite trajectory optimization.

Gym environment that simulates a small chip satellite in space

A satellite environment for OpenAI gym.

Challenging reinforcement learning environments with locomotion tasks in space

RL environments and tools for spacecraft autonomy research, built on Basilisk. Developed by the AVS Lab.

[IEEE T-PAMI 2024] All you need for End-to-end Autonomous Driving

🌏 📡 🌏 📡 LEOGPS - Satellite Navigation with GPS on Python!

The On-board Artificial Intelligence Research (OnAIR) Platform is a framework that enables AI algorithms written in Python to interact with NASA's cFS. It is intended to explore research concepts i…

Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)

A short and easy implementation of Quantile Regression DQN | Distributional Reinforcement Learning

Deep Reinforcement Learning codes for study. Currently, there are only codes for algorithms: DQN, C51, QR-DQN, IQN, QUOTA.

The implement of all kinds of dqn reinforcement learning with Pytorch

Official implementation of the algorithmic approach presented in the research paper entitled "Risk-Sensitive Policy with Distributional Reinforcement Learning".

Implementation of 'A Distributional Perspective on Reinforcement Learning' and 'Distributional Reinforcement Learning with Quantile Regression' based on OpenAi DQN baselines.

Official code repo for the MARL book (www.marl-book.com)

For modeling simulation and control of manipulator arms on free floating spacecraft

An extra wrapper for the GMAT Python API to simplify setting up mission simulations.