Stars
[ACL 2023] One Embedder, Any Task: Instruction-Finetuned Text Embeddings
Related papers for reinforcement learning, including classic papers and latest papers in top conferences
In Generative AI with Large Language Models (LLMs), you’ll learn the fundamentals of how generative AI works, and how to deploy it in real-world applications.
Code and written solutions of the assignments of the Stanford CS224N: Natural Language Processing with Deep Learning course from winter 2022/2023
This repository contains code and data for the paper of conditional generative adversarial imitation learning (cGAIL) accepted by ICDM2019.
Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration
The paper list of the 86-page paper "The Rise and Potential of Large Language Model Based Agents: A Survey" by Zhiheng Xi et al.
Real-World RL Benchmark Suite
(NeurIPS '21 Spotlight) IQ-Learn: Inverse Q-Learning for Imitation
Resource Constrained Deep Reinforcement Learning With Gym-BSS Environment
Emergency Response System (ERS) (Ambualance allocation problem) simualtor gym environment.
This is the homepage of a new book entitled "Mathematical Foundations of Reinforcement Learning."
The repository is for safe reinforcement learning baselines.
Collection of some revenue management tools for python 3
Google Research
Code for the paper "Generative Adversarial Imitation Learning"
Massively Parallel Deep Reinforcement Learning. 🔥
Clean PyTorch implementations of imitation and reward learning algorithms
Stable-Baselines tutorial for Journées Nationales de la Recherche en Robotique 2019
ILSwiss is an Easy-to-run Imitation Learning (IL, or Learning from Demonstration, LfD) and also Reinforcement Learning (RL) framework (template) in PyTorch.
OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
Basic constrained RL agents used in experiments for the "Benchmarking Safe Exploration in Deep Reinforcement Learning" paper.
Pytorch Implementation for First Order Constrained Optimization in Policy Space (FOCOPS).
PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and RL