Codebase for the self-supervised goal reaching benchmark introduced in the LEXA paper

Last update: Dec 22, 2022

Related tags

Overview

LEXA Benchmark

Codebase for the self-supervised goal reaching benchmark introduced in the LEXA paper (Discovering and Achieving Goals via World Models, NeurIPS 2021).

Setup

Create the conda environment by running : conda env create -f environment.yml

Alternatively, you can update an existing conda environment by running : conda env update -f environment.yml

Modify the python path
export PYTHONPATH=

Export the following variables for rendering
export MUJOCO_RENDERER=egl; export MUJOCO_GL=egl

Please follow these instructions to install mujoco

Bibtex

If you find this code useful, please cite:

@misc{lexa2021,
    title={Discovering and Achieving Goals via World Models},
    author={Mendonca, Russell and Rybkin, Oleh and
    Daniilidis, Kostas and Hafner, Danijar and Pathak, Deepak},
    year={2021},
    Booktitle={NeurIPS}
}

Acknowledgements

This benchmark is built on top of the following environments: Adept, MetaWorld, and DeepMind Control Suite.

Unified Pre-training for Self-Supervised Learning and Supervised Learning for ASR

UniSpeech The family of UniSpeech: UniSpeech (ICML 2021): Unified Pre-training for Self-Supervised Learning and Supervised Learning for ASR UniSpeech-

282 Jan 9, 2023

This project uses reinforcement learning on stock market and agent tries to learn trading. The goal is to check if the agent can learn to read tape. The project is dedicated to hero in life great Jesse Livermore.

Reinforcement-trading This project uses Reinforcement learning on stock market and agent tries to learn trading. The goal is to check if the agent can

1.4k Dec 22, 2022

Narya API allows you track soccer player from camera inputs, and evaluate them with an Expected Discounted Goal (EDG) Agent

Narya The Narya API allows you track soccer player from camera inputs, and evaluate them with an Expected Discounted Goal (EDG) Agent. This repository

121 Dec 30, 2022

Feed forward VQGAN-CLIP model, where the goal is to eliminate the need for optimizing the latent space of VQGAN for each input prompt

Feed forward VQGAN-CLIP model, where the goal is to eliminate the need for optimizing the latent space of VQGAN for each input prompt. This is done by

135 Dec 30, 2022

Angora is a mutation-based fuzzer. The main goal of Angora is to increase branch coverage by solving path constraints without symbolic execution.

Angora Angora is a mutation-based coverage guided fuzzer. The main goal of Angora is to increase branch coverage by solving path constraints without s

833 Jan 7, 2023

Learning Domain Invariant Representations in Goal-conditioned Block MDPs

Learning Domain Invariant Representations in Goal-conditioned Block MDPs Beining Han, Chongyi Zheng, Harris Chan, Keiran Paster, Michael R. Zhang, Jim

3 Apr 12, 2022

A computational optimization project towards the goal of gerrymandering the results of a hypothetical election in the UK.

1 Jan 18, 2022

Group project for MFIN7036. Our goal is to predict firm profitability with text-based competition measures.

NLP_0-project Group project for MFIN7036. Our goal is to predict firm profitability with text-based competition measures1. We are a "democratic" and c

3 Mar 16, 2022

Codebase for the Summary Loop paper at ACL2020

Summary Loop This repository contains the code for ACL2020 paper: The Summary Loop: Learning to Write Abstractive Summaries Without Examples. Training

Canny Lab @ The University of California, Berkeley

44 Nov 4, 2022

Comments

Problem in distance function / success criterion for dmc walker
I noticed a problem in the distance function when running experiments on the dmc_walker_walk task.

from lexa_benchmark.envs.dmc import DmcEnv env = DmcEnv('walker_walk') final_state = [-0.56107332, 0.20019225, 0.6347682, 1.75441328, 0.00908487, 0.7944932, 0.74205822, -1.85826755, -0.79472572] env._compute_reward(goal_idx=9, pose=final_state)

The above code will output (-0.6347682, 1.0) which means that the final distance to the goal is 0.6347682 and that the episode is considered successful.

However, the final_state corresponds to the following observation:

and the goal is:

So we clearly see that the distance function and/or success threshold should be defined differently to assess whether the goal was achieved or not.
opened by LinaMezghani 1

Owner

Oleg Rybkin

Ph.D. student with Kostas Daniilidis. I work on making machines think about the future.

GitHub

This is the official repository for evaluation on the NoW Benchmark Dataset. The goal of the NoW benchmark is to introduce a standard evaluation metric to measure the accuracy and robustness of 3D face reconstruction methods from a single image under variations in viewing angle, lighting, and common occlusions.

NoW Evaluation This is the official repository for evaluation on the NoW Benchmark Dataset. The goal of the NoW benchmark is to introduce a standard e

71 Dec 30, 2022

A benchmark dataset for mesh multi-label-classification based on cube engravings introduced in MeshCNN

Double Cube Engravings This script creates a dataset for multi-label mesh clasification, with an intentionally difficult setup for point cloud classif

1 Nov 30, 2021

The Self-Supervised Learner can be used to train a classifier with fewer labeled examples needed using self-supervised learning.

Published by SpaceML • About SpaceML • Quick Colab Example Self-Supervised Learner The Self-Supervised Learner can be used to train a classifier with

92 Nov 30, 2022

Utility tools for the "Divide and Remaster" dataset, introduced as part of the Cocktail Fork problem paper

Divide and Remaster Utility Tools Utility tools for the "Divide and Remaster" dataset, introduced as part of the Cocktail Fork problem paper The DnR d

46 Dec 11, 2022

A weakly-supervised scene graph generation codebase. The implementation of our CVPR2021 paper ``Linguistic Structures as Weak Supervision for Visual Scene Graph Generation''

README.md shall be finished soon. WSSGG 0 Overview 1 Installation 1.1 Faster-RCNN 1.2 Language Parser 1.3 GloVe Embeddings 2 Settings 2.1 VG-GT-Graph

35 Nov 20, 2022

This repository contains the code for using the H3DS dataset introduced in H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction

H3DS Dataset This repository contains the code for using the H3DS dataset introduced in H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction Access

72 Dec 10, 2022

A commany has recently introduced a new type of bidding, the average bidding, as an alternative to the bid given to the current maximum bidding

Business Problem A commany has recently introduced a new type of bidding, the average bidding, as an alternative to the bid given to the current maxim

1 Jan 15, 2022

This is the official code of our paper "Diversity-based Trajectory and Goal Selection with Hindsight Experience Relay" (PRICAI 2021)

Diversity-based Trajectory and Goal Selection with Hindsight Experience Replay This is the official implementation of our paper "Diversity-based Traje

6 Jul 18, 2022

[CVPR 2021] "The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models" Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Michael Carbin, Zhangyang Wang

The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models Codes for this paper The Lottery Tickets Hypo

59 Dec 28, 2022

Patch Rotation: A Self-Supervised Auxiliary Task for Robustness and Accuracy of Supervised Models

Patch-Rotation(PatchRot) Patch Rotation: A Self-Supervised Auxiliary Task for Robustness and Accuracy of Supervised Models Submitted to Neurips2021 To

4 Jul 12, 2021