Code for the paper "Language Models are Unsupervised Multitask Learners"

A python package to interact with and scrape the StoryGraph website.

[CoRL 2022] InterFuser: Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer

Bayesian optimisation & Reinforcement Learning library developed by Huawei Noah's Ark Lab

Repository for the paper "Verifying Reinforcement Learning up to Inifinity"

nl2spec: Interactively Translating Unstructured Natural Language to Temporal Logics with Large Language Models

High-throughput simulation platform for Artificial Intelligence reseach

A course in reinforcement learning in the wild

VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set …

Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools, with 10x faster training through evolutionary hyperparameter optimization.

Interactive field-aligned mesh generator

Executive control code for STRANDS robots.

One repository is all that is necessary for Multi-agent Reinforcement Learning (MARL)

Dave's fork of the main development version of the PRISM model checker. http://www.prismmodelchecker.org/

Modularized Implementation of Deep RL Algorithms in PyTorch

[PLDI 19'] An Inductive Synthesis Framework for Verifiable Reinforcement Learning

Risk-Averse Distributional Reinforcement Learning: Code

Basic constrained RL agents used in experiments for the "Benchmarking Safe Exploration in Deep Reinforcement Learning" paper.

An extension of the PyMARL codebase that includes additional algorithms and environment support

From search engines, to science, to robotics, this reposity is meant to showcase the use of reinforcement learning in the world..

This is the official implementation of Multi-Agent PPO (MAPPO).

Markov Decision Processes, Dynamic Programming and Reinforcement Learning

📊 Save matplotlib figures as TikZ/PGFplots for smooth integration into LaTeX.

Code for the paper "AlwaysSafe: Reinforcement Learning Without Safety Constraint Violations During Training"

Code to reproduce results in the AAAI 2018 paper "Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning" by Daniel S. Brown and Scott Niekum.

Code for CORL'18 paper "Risk-Aware Active Inverse Reinforcement Learning"

Level-Based Foraging (LBF): A multi-agent reinforcement learning environment

Level-based Foraging (LBF): A multi-agent environment for RL