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Snowflake

This repo contains software for our paper: Snowflake: Scaling GNNs to High-Dimensional Continuous Control via Parameter Freezing.

Our code builds the NerveNet codebase written for NerveNet: Learning Structured Policy with Graph Neural Networks by Wang et al., 2018.

Build

To set up Snowflake, we recommend the use of the docker scripts contained here.

Requirements

One pre-requisite is the possession of MuJoCo license, in the form of a file named mjkey.txt. Our Dockerfile expects this to be placed in the root of this repo (i.e. snowflake/mjkey.txt). This is not supplied and must be provided by the user.

Docker Build

To install the necessary dependencies, run:

./docker_build.sh

This will run our Dockerfile to create an image named snowflake. If you wish to install without the use of Docker then consult our Dockerfile for the required dependencies.

(Note: Owing to our use of MuJoCo there are quite a few dependencies required.)

This Dockerfile is set up to run Snowflake on CPUs. In most cases MuJoCo is CPU intensive enough that a GPU is of little benefit. If one does wish to use a GPU then modifications to the Dockerfile will be required.

Usage

Docker Run

The next step is to run:

./docker_run.sh

This creates and enters a container, mounting this Snowflake repo as the working directory. As this is a mounted volume, changes made to files in this repo within the container will persist on the main system.

From within this container, to use Snowflake, run:

python main.py [--configs...]

Configuration

To see what configuration options are available, either run python main.py --help, or consult util/config.py.

For example,

python main.py
  --task CentipedeSix-v1
  --thread_agents CentipedeSix-v1 CentipedeSix-v1
  --network_shape 64,64
  --lr 0.0003
  --timesteps_per_batch 2048
  --optim_batch_size 256
  --label example_run

will run Centipede-6 across two threads, with a 64x64 message function, a learning rate of 0.003, a batch size of 2048 and minibatch size of 256.

Each of these configurations are set to have a default which typically matches the standard value used in our experiments (although this may vary across tasks). Simply running python main.py with no configurations will train a Centipede-6 using the default settings.

Output

Results and training statistics will be printed to standard output, and also recorded in the log/ directory.

They are also recorded in the summary/ directory, in a format that can be used with Tensorboard, e.g. tensorboard --logdir summary/.

Checkpoints of the model are also saved periodically in the checkpoint/ directory. The model can be loaded into the agent using the command python main.py --ckpt_name checkpoint/etc.... This can either be used to resume stopped training runs, or load a model trained on one task into the agent of another task.