代码地址：https://github.com/jayLEE0301/vq_bet_official.git
创建环境
conda create -n vq-bet python=3.9
conda activate vq-bet
拉取库
git clone https://github.com/jayLEE0301/vq_bet_official.git
export PROJ_ROOT=$(pwd)
安装pytorch
conda install pytorch==1.12.1 torchvision==0.13.1 cudatoolkit=11.3 -c pytorch
安装依赖
cd vq_bet_official
pip install -r requirements.txt
pip install -e .
安装mujoco
mkdir -p /root/.mujoco \
wget https://mujoco.org/download/mujoco210-linux-x86_64.tar.gz -O mujoco.tar.gz \
tar -xf mujoco.tar.gz -C /root/.mujoco \
rm mujoco.tar.gz
nano ~/.bashrc
# 添加
export LD_LIBRARY_PATH=/root/.mujoco/mujoco210/bin:$LD_LIBRARY_PATH
source ~/.bashrc
或者直接修改dockerfile，重建一下docker，一步到位：
RUN mkdir -p /root/.mujoco \&& wget https://mujoco.org/download/mujoco210-linux-x86_64.tar.gz -O mujoco.tar.gz \&& tar -xf mujoco.tar.gz -C /root/.mujoco \&& rm mujoco.tar.gzENV LD_LIBRARY_PATH /root/.mujoco/mujoco210/bin:${LD_LIBRARY_PATH}
安装对应版本的mujoco-py
pip install mujoco-py==2.1.2.14
安装D4RL
cd ..
git clone https://github.com/Farama-Foundation/d4rl.git
cd d4rl
pip install -e .
cd ../vq_bet_official
UR3环境安装
cd $PROJ_ROOT/vq_bet_official/envs/ur3
pip install -e .
cd $PROJ_ROOT/vq_bet_official
运行
下载数据集
mkdir data
cd data
gdown --fuzzy https://drive.google.com/file/d/1aHb4kV0mpMvuuApBpVGYjAPs6MCNVTNb/view?usp=sharing
修改 ./examples/configs/env_vars/env_vars.yaml 中，对应路径
下载权重
mkdir checkpoint
cd checkpoint
gdown --fuzzy https://drive.google.com/file/d/1iGRyxwPHMsSVDFGojTiPteU3NVNNXMfP/view?usp=sharing
修改 ./examples/configs/train_kitchen_goalcond.yaml
vqvae_load_dir: YOUR_PATH_TO_DOWNLOADED_WEIGHTS/rvq/trained_vqvae.pt
load_path: YOUR_PATH_TO_DOWNLOADED_WEIGHTS/vq-bet
Then, set config_name=“train_kitchen_goalcond” in ./examples/train.py and run train.py.
python examples/train.py
# 报错 gladLoadGL error 用下面这个
MUJOCO_GL=egl CUDA_VISIBLE_DEVICES=0 python examples/train.py
使用wandb
报错
Cython.Compiler.Errors.CompileError
pip install "cython<3"
patchelf 错误
pip install patchelf
用到wandb的地方把entity注释掉，防止莫名报错
