Welcome to the official GitHub repository for the remastered version of EfficientZero, an advanced Reinforcement Learning model. EfficientZero Remastered is a production-ready release that builds upon the original research model, offering enhanced performance and stability for applications such as game AI, meta learning, optimization, large language models (LLMs), generative models, and more.
- A100 GPU Training Compatibility
- Comprehensive Preemption Recovery
- Replay Buffer Checkpointing System
- Updated Dependencies for Improved Stability
- Streamlined CPU to GPU Memory Allocation
- Addressed Crashes on Latest Video Cards
- Automatic Batch Worker Restart after Failures
- Refined Error-Handling and Messaging
- Access to Pre-Trained Models
- Guidelines for Integrating New Gym Environments
- Dedicated FAQ and Forum for Production Users
EfficientZero Remastered is supported in part by a grant from stability.ai. Please note that we are not the original creators of the EfficientZero model, but we have extensively refined and optimized the code for production environments. This project is still under active development.
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.
Before starting training, you need to build the c++/cython style external packages. (GCC version 7.5+ is required.)
cd core/ctree
bash make.sh
If you're using a brand new graphics card, you'll need to install the latest version of pytorch. Visit PyTorch.org: Get started and choose the latest cuda version, and install it with pip or pipenv. You may need to reinstall pytorch if you've already installed an older version.
E.g. The PyTorch website recommended I run this for my RTX 3080 laptop (Jul 2022).
pipenv install --index https://download.pytorch.org/whl/cu116 "torch==1.12.0+cu116" "torchvision==0.13.0+cu116" "torchaudio==0.12.0+cu116"
To install the (non-driver) dependencies for this, I recommend using pipenv:
pipenv --three
pipenv install -r requirements.txt
- Train:
pipenv run python main.py --env ALE/Breakout-v5 --case atari --opr train --amp_type torch_amp --num_gpus 1 --num_cpus 10 --cpu_actor 1 --gpu_actor 1 --force - Test:
pipenv run python main.py --env ALE/Breakout-v5 --case atari --opr test --amp_type torch_amp --num_gpus 1 --load_model --model_path model.p \
We provide train.sh and test.sh for training and evaluation.
- Train:
- With 4 GPUs (3090):
bash train.sh
- With 4 GPUs (3090):
- Test:
pipenv run 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 |
The architecture of the training pipeline is shown as follows:
- 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)) incore/reanalyze_worker.py.
If you wan to apply EfficientZero to a new environment like mujoco. Here are the steps for registration:
- Follow the directory
config/atariand create dir for the env atconfig/mujoco. - Implement your
MujocoConfig(BaseConfig)class and implement the models as well as your environment wrapper. - Register the case at
main.py.
Evaluation with 32 seeds for 3 different runs (different seeds).
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}
}
If you have any question or want to use the code, please contact [email protected] .
We appreciate the following github repos a lot for their valuable code base implementations:
https://github.com/koulanurag/muzero-pytorch