PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

OpenDILab Decision AI Engine

LLMTechSite, 专注于通用人工智能领域的技术生态。

Contains a few projects I completed while taking the Intro to AI course

This repository contains Dongming Shen's code and documentation for the research projects conducted at the AIDyS Lab, USC. The project focuses on integrating Reinforcement Learning (RL) to solve partially observable Markov decision processes (POMDP) under finite linear temporal logic (LTL) constraints.

Implementation of RL concepts in simplified way!

Reinforcement learning on 2D video game

🤖 The Full Process Python Package for Robot Learning from Demonstration and Robot Manipulation

The Core Reinforcement Learning library is intended to enable scalable deep reinforcement learning experimentation in a manner extensible to new simulations and new ways for the learning agents to interact with them. The hope is that this makes RL research easier by removing lock-in to particular simulations.The work is released under the follow…

Four python code files experimenting with reinforcement learning

A curated list of awesome model based RL resources (continually updated)

Reinforcement learning algorithms

Policy Iteration for Continuous Dynamics

Library for reinforcement learning with c++

This is JAX implementation of Book of Foundations of Reinforcement Learning with Applications in Finance by Ashwin Rao & Tikhon Jelvis

🏛️A research-friendly codebase for fast experimentation of single-agent reinforcement learning in JAX • End-to-End JAX RL

Reinforcement Learning based Artificial Pancreas Systems for Controlling Blood Glucose in Type 1 Diabetes.

The GOLang implementation of NeuroEvolution of Augmented Topologies (NEAT) method to evolve and train Artificial Neural Networks without error back propagation

This project utilizes a guide robot to aid visually impaired individuals in navigating obstacle-filled maps. Advanced agents like Behiavor Cloning and Advantage-Filtered Behavior Cloning optimize navigation, while human-human interaction enhances data collection for improved safety and efficiency.

Contrib package for Stable-Baselines3 - Experimental reinforcement learning (RL) code