Jiminy Cricket Environment (NeurIPS 2021)

Overview

Jiminy Cricket

This is the repository for "What Would Jiminy Cricket Do? Towards Agents That Behave Morally" by Dan Hendrycks*, Mantas Mazeika*, Andy Zou, Sahil Patel, Christine Zhu, Jesus Navarro, Dawn Song, Bo Li, and Jacob Steinhardt.

How To Use

To initialize an environment:

import sys
sys.path.insert(0, '<your path here>/jiminy-cricket')
from annotated_env import AnnotatedEnv

game_name = 'zork1'  # change to desired game
env = AnnotatedEnv(game_folder_path='<your path here>/jiminy-cricket/annotated_games/{}'.format(game_name))

Contents

  • annotated_games: This contains the game folders for Jiminy Cricket. The path to each game folder can be passed to AnnotatedEnv via game_folder_path to select an environment to initialize.
  • examples: This contains scripts with examples of using Jiminy Cricket, including experiment code for the paper.
  • extras: This contains additional source code and scripts used for annotation.

Citation

If you find this useful in your research, please consider citing:

@article{hendrycks2021jiminycricket,
  title={What Would Jiminy Cricket Do? Towards Agents That Behave Morally},
  author={Dan Hendrycks and Mantas Mazeika and Andy Zou and Sahil Patel and Christine Zhu and Jesus Navarro and Dawn Song and Bo Li and Jacob Steinhardt},
  journal={NeurIPS},
  year={2021}
}
Owner
Dan Hendrycks
PhD student at UC Berkeley.
Dan Hendrycks
PyTorch implementation for View-Guided Point Cloud Completion

PyTorch implementation for View-Guided Point Cloud Completion

22 Jan 04, 2023
The official repository for paper ''Domain Generalization for Vision-based Driving Trajectory Generation'' submitted to ICRA 2022

DG-TrajGen The official repository for paper ''Domain Generalization for Vision-based Driving Trajectory Generation'' submitted to ICRA 2022. Our Meth

Wang 25 Sep 26, 2022
Evaluation framework for testing segmentation networks in PyTorch

Evaluation framework for testing segmentation networks in PyTorch. What segmentation network to choose for next Kaggle competition? This benchmark knows the answer!

Eugene Khvedchenya 37 Apr 27, 2022
Pathdreamer: A World Model for Indoor Navigation

Pathdreamer: A World Model for Indoor Navigation This repository hosts the open source code for Pathdreamer, to be presented at ICCV 2021. Paper | Pro

Google Research 122 Jan 04, 2023
Atif Hassan 103 Dec 14, 2022
GANSketchingJittor - Implementation of Sketch Your Own GAN in Jittor

GANSketching in Jittor Implementation of (Sketch Your Own GAN) in Jittor(计图). Or

Bernard Tan 10 Jul 02, 2022
Lacmus is a cross-platform application that helps to find people who are lost in the forest using computer vision and neural networks.

lacmus The program for searching through photos from the air of lost people in the forest using Retina Net neural nwtwork. The project is being develo

Lacmus Foundation 168 Dec 27, 2022
Square Root Bundle Adjustment for Large-Scale Reconstruction

RootBA: Square Root Bundle Adjustment Project Page | Paper | Poster | Video | Code Table of Contents Citation Dependencies Installing dependencies on

Nikolaus Demmel 205 Dec 20, 2022
A benchmark dataset for emulating atmospheric radiative transfer in weather and climate models with machine learning (NeurIPS 2021 Datasets and Benchmarks Track)

ClimART - A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models Official PyTorch Implementation Using deep le

21 Dec 31, 2022
The implementation of "Optimizing Shoulder to Shoulder: A Coordinated Sub-Band Fusion Model for Real-Time Full-Band Speech Enhancement"

SF-Net for fullband SE This is the repo of the manuscript "Optimizing Shoulder to Shoulder: A Coordinated Sub-Band Fusion Model for Real-Time Full-Ban

Guochen Yu 36 Dec 02, 2022
Python Implementation of algorithms in Graph Mining, e.g., Recommendation, Collaborative Filtering, Community Detection, Spectral Clustering, Modularity Maximization, co-authorship networks.

Graph Mining Author: Jiayi Chen Time: April 2021 Implemented Algorithms: Network: Scrabing Data, Network Construbtion and Network Measurement (e.g., P

Jiayi Chen 3 Mar 03, 2022
Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.

Modeling High-Frequency Limit Order Book Dynamics Using Machine Learning Framework to capture the dynamics of high-frequency limit order books. Overvi

Chang-Shu Chung 1.3k Jan 07, 2023
🙄 Difficult algorithm, Simple code.

🎉TensorFlow2.0-Examples🎉! "Talk is cheap, show me the code." ----- Linus Torvalds Created by YunYang1994 This tutorial was designed for easily divin

1.7k Dec 25, 2022
Hierarchical Clustering: O(1)-Approximation for Well-Clustered Graphs

Hierarchical Clustering: O(1)-Approximation for Well-Clustered Graphs This repository contains code to accompany the paper "Hierarchical Clustering: O

3 Sep 25, 2022
City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones Code

City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones Requirements Python 3.8 or later with all requirements.txt dependencies installed,

88 Dec 12, 2022
Minimal implementation and experiments of "No-Transaction Band Network: A Neural Network Architecture for Efficient Deep Hedging".

No-Transaction Band Network: A Neural Network Architecture for Efficient Deep Hedging Minimal implementation and experiments of "No-Transaction Band N

19 Jan 03, 2023
Attentive Implicit Representation Networks (AIR-Nets)

Attentive Implicit Representation Networks (AIR-Nets) Preprint | Supplementary | Accepted at the International Conference on 3D Vision (3DV) teaser.mo

29 Dec 07, 2022
Official implementation for paper: Feature-Style Encoder for Style-Based GAN Inversion

Feature-Style Encoder for Style-Based GAN Inversion Official implementation for paper: Feature-Style Encoder for Style-Based GAN Inversion. Code will

InterDigital 63 Jan 03, 2023
Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics

Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics

14 Nov 06, 2022
Deduplicating Training Data Makes Language Models Better

Deduplicating Training Data Makes Language Models Better This repository contains code to deduplicate language model datasets as descrbed in the paper

Google Research 431 Dec 27, 2022