Open-source codebase for EfficientZero, from "Mastering Atari Games with Limited Data" at NeurIPS 2021.

Overview

EfficientZero (NeurIPS 2021)

Open-source codebase for EfficientZero, from "Mastering Atari Games with Limited Data" at NeurIPS 2021.

Environments

EfficientZero requires python3 (>=3.6) and pytorch (>=1.8.0) with the development headers.

We recommend to use torch amp (--amp_type torch_amp) to accelerate training.

Prerequisites

Before starting training, you need to build the c++/cython style external packages.

cd core/ctree
bash make.sh

The distributed framework of this codebase is built on ray.

Installation

As for other packages required for this codebase, please run pip install -r requirements.txt.

Usage

Quick start

  • Train: python main.py --env BreakoutNoFrameskip-v4 --case atari --opr train --amp_type torch_amp --num_gpus 1 --num_cpus 10 --cpu_actor 1 --gpu_actor 1 --force
  • Test: python main.py --env BreakoutNoFrameskip-v4 --case atari --opr test --amp_type torch_amp --num_gpus 1 --load_model --model_path model.p \

Bash file

We provide train.sh and test.sh for training and evaluation.

  • Train:
    • With 4 GPUs (3090): bash train.sh
  • Test: bash test.sh
Required Arguments Description
--env Name of the environment
--case {atari} It's used for switching between different domains(default: atari)
--opr {train,test} select the operation to be performed
--amp_type {torch_amp,none} use torch amp for acceleration
Other Arguments Description
--force will rewrite the result directory
--num_gpus 4 how many GPUs are available
--num_cpus 96 how many CPUs are available
--cpu_actor 14 how many cpu workers
--gpu_actor 20 how many gpu workers
--seed 0 the seed
--use_priority use priority in replay buffer sampling
--use_max_priority use the max priority for the newly collectted data
--amp_type 'torch_amp' use torch amp for acceleration
--info 'EZ-V0' some tags for you experiments
--p_mcts_num 8 set the parallel number of envs in self-play
--revisit_policy_search_rate 0.99 set the rate of reanalyzing policies
--use_root_value use root values in value targets (require more GPU actors)
--render render in evaluation
--save_video save videos for evaluation

Architecture Designs

The architecture of the training pipeline is shown as follows:

Some suggestions

  • To use a smaller model, you can choose smaller dim of the projection layers (Eg: 256/64) and the LSTM hidden layer (Eg: 64) in the config.
  • For GPUs with 10G memory instead of 20G memory, you can allocate 0.25 gpu for each GPU maker (@ray.remote(num_gpus=0.25)) in core/reanalyze_worker.py.

New environment registration

If you wan to apply EfficientZero to a new environment like mujoco. Here are the steps for registration:

  1. Follow the directory config/atari and create dir for the env at config/mujoco.
  2. Implement your MujocoConfig(BaseConfig) class and implement the models as well as your environment wrapper.
  3. Register the case at main.py.

Results

Evaluation with 32 seeds for 3 different runs (different seeds).

Citation

If you find this repo useful, please cite our paper:

@inproceedings{ye2021mastering,
  title={Mastering Atari Games with Limited Data},
  author={Weirui Ye, and Shaohuai Liu, and Thanard Kurutach, and Pieter Abbeel, and Yang Gao},
  booktitle={NeurIPS},
  year={2021}
}

Contact

If you have any question or want to use the code, please contact [email protected] .

Acknowledgement

We appreciate the following github repos a lot for their valuable code base implementations:

https://github.com/koulanurag/muzero-pytorch

https://github.com/werner-duvaud/muzero-general

https://github.com/pytorch/ELF

Owner
Weirui Ye
Weirui Ye
Using PyTorch Perform intent classification using three different models to see which one is better for this task

Using PyTorch Perform intent classification using three different models to see which one is better for this task

Yoel Graumann 1 Feb 14, 2022
A PyTorch implementation of the paper "Semantic Image Synthesis via Adversarial Learning" in ICCV 2017

Semantic Image Synthesis via Adversarial Learning This is a PyTorch implementation of the paper Semantic Image Synthesis via Adversarial Learning. Req

Seonghyeon Nam 146 Nov 25, 2022
Repository for the Bias Benchmark for QA dataset.

BBQ Repository for the Bias Benchmark for QA dataset. Authors: Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Tho

ML² AT CILVR 18 Nov 18, 2022
A package, and script, to perform imaging transcriptomics on a neuroimaging scan.

Imaging Transcriptomics Imaging transcriptomics is a methodology that allows to identify patterns of correlation between gene expression and some prop

Alessio Giacomel 10 Dec 27, 2022
An automated facial recognition based attendance system (desktop application)

Facial_Recognition_based_Attendance_System An automated facial recognition based attendance system (desktop application) Made using Python, Tkinter an

1 Jun 21, 2022
Forecasting Nonverbal Social Signals during Dyadic Interactions with Generative Adversarial Neural Networks

ForecastingNonverbalSignals This is the implementation for the paper Forecasting Nonverbal Social Signals during Dyadic Interactions with Generative A

1 Feb 10, 2022
Code for unmixing audio signals in four different stems "drums, bass, vocals, others". The code is adapted from "Jukebox: A Generative Model for Music"

Status: Archive (code is provided as-is, no updates expected) Disclaimer This code is a based on "Jukebox: A Generative Model for Music" Paper We adju

Wadhah Zai El Amri 24 Dec 29, 2022
dataset for ECCV 2020 "Motion Capture from Internet Videos"

Motion Capture from Internet Videos Motion Capture from Internet Videos Junting Dong*, Qing Shuai*, Yuanqing Zhang, Xian Liu, Xiaowei Zhou, Hujun Bao

ZJU3DV 98 Dec 07, 2022
maximal update parametrization (µP)

Maximal Update Parametrization (μP) and Hyperparameter Transfer (μTransfer) Paper link | Blog link In Tensor Programs V: Tuning Large Neural Networks

Microsoft 694 Jan 03, 2023
STYLER: Style Factor Modeling with Rapidity and Robustness via Speech Decomposition for Expressive and Controllable Neural Text to Speech

STYLER: Style Factor Modeling with Rapidity and Robustness via Speech Decomposition for Expressive and Controllable Neural Text to Speech Keon Lee, Ky

Keon Lee 114 Dec 12, 2022
Unet network with mean teacher for altrasound image segmentation

Unet network with mean teacher for altrasound image segmentation

5 Nov 21, 2022
Official pytorch implement for “Transformer-Based Source-Free Domain Adaptation”

Official implementation for TransDA Official pytorch implement for “Transformer-Based Source-Free Domain Adaptation”. Overview: Result: Prerequisites:

stanley 54 Dec 22, 2022
Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion (CVPR'2021, Oral)

DSA^2 F: Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion (CVPR'2021, Oral) This repo is the official imp

如今我已剑指天涯 46 Dec 21, 2022
General Virtual Sketching Framework for Vector Line Art (SIGGRAPH 2021)

General Virtual Sketching Framework for Vector Line Art - SIGGRAPH 2021 Paper | Project Page Outline Dependencies Testing with Trained Weights Trainin

Haoran MO 118 Dec 27, 2022
The Adapter-Bot: All-In-One Controllable Conversational Model

The Adapter-Bot: All-In-One Controllable Conversational Model This is the implementation of the paper: The Adapter-Bot: All-In-One Controllable Conver

CAiRE 37 Nov 04, 2022
Keras udrl - Keras implementation of Upside Down Reinforcement Learning

keras_udrl Keras implementation of Upside Down Reinforcement Learning This is me

Eder Santana 7 Jan 24, 2022
Apply AnimeGAN-v2 across frames of a video clip

title emoji colorFrom colorTo sdk app_file pinned AnimeGAN-v2 For Videos 🔥 blue red gradio app.py false AnimeGAN-v2 For Videos Apply AnimeGAN-v2 acro

Nathan Raw 36 Oct 18, 2022
Official implementation of the paper Vision Transformer with Progressive Sampling, ICCV 2021.

Vision Transformer with Progressive Sampling This is the official implementation of the paper Vision Transformer with Progressive Sampling, ICCV 2021.

yuexy 123 Jan 01, 2023
BARF: Bundle-Adjusting Neural Radiance Fields 🤮 (ICCV 2021 oral)

BARF 🤮 : Bundle-Adjusting Neural Radiance Fields Chen-Hsuan Lin, Wei-Chiu Ma, Antonio Torralba, and Simon Lucey IEEE International Conference on Comp

Chen-Hsuan Lin 539 Dec 28, 2022
The codes I made while I practiced various TensorFlow examples

TensorFlow_Exercises The codes I made while I practiced various TensorFlow examples About the codes I didn't create these codes by myself, but re-crea

Terry Taewoong Um 614 Dec 08, 2022