Code for Learning Space Partitions for Path Planning (http://arxiv.org/abs/2106.10544) by Kevin Yang, Tianjun Zhang, Chris Cummins, Brandon Cui, Benoit Steiner, Linnan Wang, Joseph E. Gonzalez, Dan Klein, and Yuandong Tian. See slides and transcript for presentation in neurips2021.pptx and transcript.txt respectively.
Base LaMCTS code originally from https://github.com/facebookresearch/LaMCTS . MiniWorld and MiniGrid envs originally from https://github.com/maximecb/gym-miniworld and https://github.com/maximecb/gym-minigrid respectively.
Required packages in requirements.txt. Additionally, install pytorch using conda; version 1.9.1 works fine. Install the lamcts-planning package via setup.py and also install the latent-plan directory.
Install the gym-miniworld and/or gym-minigrid directories, which have our modifications from the original envs. Make sure to install the ones included in this folder, not the official versions. Otherwise you will see crashes.
Install the code for the latent space from https://github.com/wengong-jin/hgraph2graph/tree/e396dbaf43f9d4ac2ee2568a4d5f93ad9e78b767. Note this code assumes the use of a GPU. Our code that interfaces with the pretrained latent space assumes that you put the hgraph2graph code in the same top-level directory as the top-level plalam repository. Additionally, download our pretrained latent space ckpts by running wget https://plalam-molecule-ckpts.s3.amazonaws.com/latent-molecule-ckpts.zip and unzip the contents, putting them inside a ckpt folder inside the hgraph2graph repository (see the paths under FakeArgs in lamcts_planning/util.py). Finally, put vocab.txt inside the hgraph2graph repository at the top level.
To setup the property evaluators, follow the instructions to install Chemprop at https://chemprop.readthedocs.io/en/latest/installation.html (in particular, make sure to get RDKit), and make sure to install version 1.2.0. Additionally, install scikit-learn==0.21.3. Then unzip the molecule-ckpts.zip file. There is no ckpt needed for QED since that's just a computationally defined property. clf_py36.pkl is the DRD2 property and should be placed in lamcts_planning. The other two folders are Chemprop ckpts for HIV and SARS, and the code currently assumes they exist in the same directory as the top-level plalam repository.
NOTE: if you see Warning: molecule dependencies not installed; install if running molecule exps it means your setup is failing somewhere; see the imports at the top of lamcts_planning/util.py.
MiniWorld example: (see https://github.com/maximecb/gym-miniworld for discussion on why the xvfb-run stuff is needed when running on Linux without rendering; also follow their setup instructions for e.g. the OpenGL dependencies)
xvfb-run -a -s "-screen 0 1024x768x24 -ac +extension GLX +render -noreset" python -u main.py --env MiniWorld-MazeS3-v0 --num_threads 1 --num_trials 1 --method lamcts-planning --Cp 2 --horizon 216 --replan_freq 216 --iterations 2000 --final_obs_split --latent --latent_model cnn --latent_ckpt None --init_sigma_mult 8
MiniGrid example:
xvfb-run -a -s "-screen 0 1024x768x24 -ac +extension GLX +render -noreset" python -u main.py --env MiniGrid-DoorKey-6x6-v0 --num_threads 1 --num_trials 1 --method lamcts-planning --Cp 2 --horizon 180 --replan_freq 180 --iterations 2000 --final_obs_split --latent --latent_model cnn --latent_ckpt None
Molecule example:
python -u -W ignore main.py --env QED --num_threads 1 --num_trials 1 --method lamcts-planning --Cp 0.5 --horizon 1 --replan_freq 1 --iterations 1000 --final_obs_split --action_seq_split --init_sigma_mult 1
Note the "horizon" is 1 here is just a code hack - we operate in a latent space which encodes the full action sequence. You might see a few failed to decode warnings, which is fine; the graph decoder we use fails on a small fraction of inputs. Just as long as it's not failing on all the inputs.