Embodied Intelligence via Learning and Evolution

Related tags

Deep Learningderl
Overview

Embodied Intelligence via Learning and Evolution

This is the code for the paper

Embodied Intelligence via Learning and Evolution
Agrim Gupta, Silvio Savarese, Surya Ganguli, Fei-Fei Li

The intertwined processes of learning and evolution in complex environmental niches have resulted in a remarkable diversity of morphological forms. Moreover, many aspects of animal intelligence are deeply embodied in these evolved morphologies. However, the principles governing relations between environmental complexity, evolved morphology, and the learnability of intelligent control, remain elusive, partially due to the substantial challenge of performing large-scale in silico experiments on evolution and learning. We introduce Deep Evolutionary Reinforcement Learning (DERL): a novel computational framework which can evolve diverse agent morphologies to learn challenging locomotion and manipulation tasks in complex environments using only low level egocentric sensory information. Leveraging DERL we demonstrate several relations between environmental complexity, morphological intelligence and the learnability of control.

Code Structure

The code consists of three main components:

  1. UNIMAL Design Space: A UNIversal aniMAL morphological design space that is both highly expressive yet also enriched for useful controllable morphologies.
  2. DERL: An efficient asynchronous method for parallelizing computations underlying learning and evolution across many compute nodes.
  3. Evolutionary environments and evaluation tasks: A set of three evolutionary environments and eight evaluation tasks.

Setup

We provide Dockerfile for easy installation and development. If you prefer to work without docker please take a look at Dockerfile and ensure that your local system has all the necessary dependencies installed.

Evolving Unimals

# Build docker container. Ensure that MuJoCo license is present: docker/mjkey.txt
./scripts/build_docker.sh
# Evolve unimals. Please change MOUNT_DIR location inside run_docker_cpu.sh
./scripts/run_docker_cpu.sh python tools/evolution.py --cfg ./configs/evo/ft_test.yml NODE_ID 0

The default parameters assume that you are running the code on 16 machines. Please ensure that each machine has a minimum of 72 CPUs. While running the script on multiple nodes you would have to ensure that NODE_ID on each machine is unique and between [0, NUM_NODES - 1].

Visualizing Environments

If you have installed all dependencies in your local machine. You can visualize the environment as follows:

python tools/terrain_builder.py --cfg configs/evo/mvt.yml

Credit

This codebase would not have been possible without the following amazing open source codebases:

  1. ikostrikov/pytorch-a2c-ppo-acktr-gail
  2. hill-a/stable-baselines
  3. deepmind/dm_control
  4. openai/multi-agent-emergence-environments
Owner
Agrim Gupta
Agrim Gupta
Marvis is Mastouri's Jarvis version of the AI-powered Python personal assistant.

Marvis v1.0 Marvis is Mastouri's Jarvis version of the AI-powered Python personal assistant. About M.A.R.V.I.S. J.A.R.V.I.S. is a fictional character

Reda Mastouri 1 Dec 29, 2021
NAVER BoostCamp Final Project

CV 14조 final project Super Resolution and Deblur module Inference code & Pretrained weight Repo SwinIR Deblur 실행 방법 streamlit run WebServer/Server_SRD

JiSeong Kim 5 Sep 06, 2022
SparseML is a libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

SparseML is a toolkit that includes APIs, CLIs, scripts and libraries that apply state-of-the-art sparsification algorithms such as pruning and quantization to any neural network. General, recipe-dri

Neural Magic 1.5k Dec 30, 2022
Code for the Population-Based Bandits Algorithm, presented at NeurIPS 2020.

Population-Based Bandits (PB2) Code for the Population-Based Bandits (PB2) Algorithm, from the paper Provably Efficient Online Hyperparameter Optimiza

Jack Parker-Holder 22 Nov 16, 2022
This repository stores the code to reproduce the results published in "TiWS-iForest: Isolation Forest in Weakly Supervised and Tiny ML scenarios"

TinyWeaklyIsolationForest This repository stores the code to reproduce the results published in "TiWS-iForest: Isolation Forest in Weakly Supervised a

2 Mar 21, 2022
Differentiable rasterization applied to 3D model simplification tasks

nvdiffmodeling Differentiable rasterization applied to 3D model simplification tasks, as described in the paper: Appearance-Driven Automatic 3D Model

NVIDIA Research Projects 336 Dec 30, 2022
DL course co-developed by YSDA, HSE and Skoltech

Deep learning course This repo supplements Deep Learning course taught at YSDA and HSE @fall'21. For previous iteration visit the spring21 branch. Lec

Yandex School of Data Analysis 1.3k Dec 30, 2022
DeOldify - A Deep Learning based project for colorizing and restoring old images (and video!)

DeOldify - A Deep Learning based project for colorizing and restoring old images (and video!)

Jason Antic 15.8k Jan 04, 2023
Towards Interpretable Deep Metric Learning with Structural Matching

DIML Created by Wenliang Zhao*, Yongming Rao*, Ziyi Wang, Jiwen Lu, Jie Zhou This repository contains PyTorch implementation for paper Towards Interpr

Wenliang Zhao 75 Nov 11, 2022
Image-based Navigation in Real-World Environments via Multiple Mid-level Representations: Fusion Models Benchmark and Efficient Evaluation

Image-based Navigation in Real-World Environments via Multiple Mid-level Representations: Fusion Models Benchmark and Efficient Evaluation This reposi

First Person Vision @ Image Processing Laboratory - University of Catania 1 Aug 21, 2022
PyTorchCV: A PyTorch-Based Framework for Deep Learning in Computer Vision.

PyTorchCV: A PyTorch-Based Framework for Deep Learning in Computer Vision @misc{CV2018, author = {Donny You ( Donny You 40 Sep 14, 2022

Pytorch implementation for Patient Knowledge Distillation for BERT Model Compression

Patient Knowledge Distillation for BERT Model Compression Knowledge distillation for BERT model Installation Run command below to install the environm

Siqi 180 Dec 19, 2022
Hands-On Machine Learning for Algorithmic Trading, published by Packt

Hands-On Machine Learning for Algorithmic Trading Hands-On Machine Learning for Algorithmic Trading, published by Packt This is the code repository fo

Packt 981 Dec 29, 2022
Official Implementation for the paper DeepFace-EMD: Re-ranking Using Patch-wise Earth Mover’s Distance Improves Out-Of-Distribution Face Identification

DeepFace-EMD: Re-ranking Using Patch-wise Earth Mover’s Distance Improves Out-Of-Distribution Face Identification Official Implementation for the pape

Anh M. Nguyen 36 Dec 28, 2022
Blind Video Temporal Consistency via Deep Video Prior

deep-video-prior (DVP) Code for NeurIPS 2020 paper: Blind Video Temporal Consistency via Deep Video Prior PyTorch implementation | paper | project web

Chenyang LEI 272 Dec 21, 2022
This project is a re-implementation of MASTER: Multi-Aspect Non-local Network for Scene Text Recognition by MMOCR

This project is a re-implementation of MASTER: Multi-Aspect Non-local Network for Scene Text Recognition by MMOCR,which is an open-source toolbox based on PyTorch. The overall architecture will be sh

Jianquan Ye 82 Nov 17, 2022
Deep Ensemble Learning with Jet-Like architecture

Ransomware analysis using DEL with jet-like architecture comprising two CNN wings, a sparse AE tail, a non-linear PCA to produce a diverse feature space, and an MLP nose

Ahsen Nazir 2 Feb 06, 2022
A semantic segmentation toolbox based on PyTorch

Introduction vedaseg is an open source semantic segmentation toolbox based on PyTorch. Features Modular Design We decompose the semantic segmentation

407 Dec 15, 2022
The official repo for OC-SORT: Observation-Centric SORT on video Multi-Object Tracking. OC-SORT is simple, online and robust to occlusion/non-linear motion.

OC-SORT Observation-Centric SORT (OC-SORT) is a pure motion-model-based multi-object tracker. It aims to improve tracking robustness in crowded scenes

Jinkun Cao 325 Jan 05, 2023
code and data for paper "GIANT: Scalable Creation of a Web-scale Ontology"

GIANT Code and data for paper "GIANT: Scalable Creation of a Web-scale Ontology" https://arxiv.org/pdf/2004.02118.pdf Please cite our paper if this pr

Excalibur 39 Dec 29, 2022