如何用语言学习构建世界模型？

博客地址：https://www.cnblogs.com/zylyehuo/ Learning to Model the World with Language 项目地址：dynalang （结合项目 README，遇到问题再看该笔记） 运行效果 部署步骤 第一步：配置 cuda、cudnn、torch nvcc -V ls -l /usr/local/ | grep cuda nvidia-smi ls ~/.local/lib/python3.8/site-packages | grep nvidia rm -rf ~/.local/lib/python3.8/site-packages/nvidia* ls ~/.local/lib/python3.8/site-packages | grep nvidia export PYTHONNOUSERSITE=1 pip install torch==2.0.1+cu117 torchvision==0.15.2+cu117 --extra-index-url https://download.pytorch.org/whl/cu117 python -c "import torch; print('PyTorch CUDA:', torch.version.cuda); print('GPU Available:', torch.cuda.is_available())" sudo cp /var/cudnn-local-repo-ubuntu2004-8.9.7.29/cudnn-local-CD2C2DD4-keyring.gpg /usr/share/keyrings/ sudo apt update sudo apt install libcudnn8 libcudnn8-dev sudo ldconfig sudo dpkg -i cudnn-local-repo-ubuntu2004-8.9.7.29_1.0-1_amd64.deb ldconfig -p | grep cudnn conda install -c conda-forge "cudnn>=8.9.1" 第二步：安装相关依赖包和源码 mkdir VLN_learning cd VLN_learning git clone git@github.com:zylyehuo/dynalang.git cd dynalang conda create -n dynalang-vln python=3.8 conda activate dynalang-vln pip install "jax[cuda11_cudnn82]==0.4.8" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html # 验证 JAX 和 GPU python3 -c "import jax; print('JAX Devices:', jax.devices())" # 验证 PyTorch 和 GPU python3 -c "import torch; print('Torch CUDA available:', torch.cuda.is_available())" # 验证 IPython 依赖是否报错 python3 -c "import IPython; print('IPython loaded successfully')" pip install homegrid cd VLN_learning/dynalang conda activate dynalang-vln pip install optax==0.1.7 pip install ruamel.yaml==0.17.40 pip install gym==0.26.2 pip install absl-py rich chex optax pip install tensorflow-cpu==2.13.1 tensorboard==2.13.0 tensorflow-probability==0.21.0 pip install "typing-extensions<4.6.0" pip install pandas python-dateutil click platformdirs importlib-resources sudo apt-get install \ libsdl-image1.2-dev libsdl-mixer1.2-dev libsdl-ttf2.0-dev \ libs