Code for the paper Inference via Interpolation: Contrastive Representations Provably Enable Planning and Inference.
- Check conda is installed and mujoco200 binaries are in path:
test -z "$CONDA_PREFIX" && wget -O Miniforge3.sh "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh" && bash Miniforge3.sh -b -p "$HOME/conda" && rm Miniforge3.sh
"$HOME/conda/bin/conda" init "$(basename $SHELL)"
test ! -e ~/.mujoco/mujoco200 && mkdir -p ~/.mujoco && wget -O mujoco200.zip https://www.roboti.us/download/mujoco200_$(test $(uname) == "Linux" && echo linux || echo macos).zip && unzip mujoco200.zip && rm mujoco200.zip && mv mujoco200* ~/.mujoco
echo "$LD_LIBRARY_PATH" | grep -q mujoco200 || echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$HOME/.mujoco/mujoco200/bin' >> ~/.bashrc
- Clone repo and build environment:
git clone https://github.com/vivekmyers/contrastive_planning.git
cd contrastive_planning
conda env create
conda activate contrastive_planning
Run the following commands to train the method and baselines discussed in the paper for a single initialization seed and dataset shuffle:
python run_train.py --env maze2d-large-v1
python run_train.py --env door-human-v0
To evaluate success rates by distance and planning MSE for our method and baselines, run the following.
python run_eval.py --env maze2d-large-v1 --n_wypt 20 --rollout
python run_eval.py --env door-human-v0 --mse --n_wypt 5
python run_eval.py --env door-human-v0 --mse --n_wypt 1
The following commands will plot the results as shown in the paper.
python run_plot.py --env maze2d-large-v1 --rollout
python run_plot.py --env door-human-v0 --waypoint_mse --n_wypt 5
python run_plot.py --env door-human-v0 --barplot --n_wypt 1
python run_plot.py --env door-human-v0 --plot_plan
All quantitative results from the paper can be reproduced by running make all. This will train and evaluate 100 seeds by default.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent