geekyutao / EfficientZero

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

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

Besides, openai baselines is also required for this codebase.

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 [e-mail] .

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