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SAE-RL

Spatial Autoencoders (SAEs) for Reinforcement Learning (RL)

This repository contains the code to reproduce the results of the paper Tracking Object Positions in Reinforcement Learning: A Metric for Keypoint Detection.

We present a computationally lightweight metric to evaluate latent representations of spatial autoencoder architectures.

Content:

  1. Installing dependencies
  2. Executing scripts
  3. More useful information

Installation

You can launch a virtual environment with pipenv or run a Docker container to execute this code in. For both options, ensure your current working directory is this repository's root directory.

Launch a pipenv environment

The following command creates a pipenv virtual environment and installs all necessary dependencies. Additionally, pass the --dev option to install packages only recommended for development.

pipenv install

Activate this virtual environment in your current shell with

pipenv shell

Run a Docker container

A docker container with all necessary dependencies can, specifying the desired tag, easily be pulled from this Docker Hub repository with

docker pull jonasreiher/sae-rl:<tag>

This is the recommended way to obtain a Docker image. Pay attention to available version specifier tags to pull your desired version.

Run an interactive container from this image, specifying the desired tag, with

docker run -it --gpus all jonasreiher/sae-rl:<tag>

Alternatively, the following command builds a Docker image locally with all necessary dependencies installed, building upon the pipenv environment.

docker build . -t jonasreiher/sae-rl:latest

You can push an updated version of this image to Docker Hub, specifying a suitable version tag, with

docker push jonasreiher/sae-rl:<tag>

Execution

With your virtual environment activated or within your container, you can run any of the scripts deposited in scripts/. It is recommended to call them from the repository root where logs will then be saved.

Note: All scripts use Hydra for configuration management. The base configuration files for everything can be found in configs/. Familiarize yourself with the possible configuration options there. To disable Hydra's directory creation and logging, pass hydra=hush.

Record a dataset

To record a dataset, make use of record.py via

python scripts/record.py \
    environment=panda-push \
    recording.n_sequences=1000

Hint: Run python scripts/record.py --help to see a list of all configuration parameters available as command line arguments.

This will store an image and keypoint dataset named PandaPush-v3_labelled.hdf5 in data/.

Paper replication

To replicate the dataset we used in the paper, run

python scripts/record.py \
    environment=panda-push-custom \
    environment.camera=panda-close \
    environment.camera.renderer=Tiny \
    save.tag=_fast

Train a spatial autoencoder

To train a spatial autoencoder on the dataset generated above, make use of train_sae.py via

python scripts/train_sae.py \
    dataset=panda-push \
    model=keynet \
    training.epochs=500 \
    wandb=off

Hint: Run python scripts/train_sae.py --help to see a list of all configuration parameters available as command line arguments.

This will store logging information, including the final SAE checkpoint, under the current timestamp in logs/sae/panda_push/keynet+keynet/. To view these logs with Tensorboard, run the following command in this repository's root directory.

tensorboard --log-dir logs/sae/

Note: To concurrently log to Weights and Biases, adjust wandb.entity and wandb.project to your profile and set wandb.enabled=True (the default when omitting wandb=off).

Paper replication

All configurations for experiments conducted in the paper are collected in configs/experiments/. To train, e.g., the "Basic-vel-std-bg" SAE (also see Naming), you can just run

python scripts/train_sae.py \
    +experiment=sae-basic-vel-var-bg

Train a reinforcement learning agent

To train a reinforcement learning agent with feature points from the spatial autoencoder trained above, make use of train_rl.py via

python scripts/train_rl.py \
    environment=panda-push \
    algorithm=sac \
    training.observation.immeasurable=False \
    training.observation.feature_points=True \
    training.sae_checkpoint=logs/sae/panda_push/keynet+keynet/YYYY-MM-DD--HH-MM-SS--0/checkpoint_final.pth \
    training.steps=1000000 \
    wandb=off

Hint: Run python scripts/train_rl.py --help to see a list of all configuration parameters available as command line arguments.

This will store logging information, including the final agent model, under the current timestamp in logs/rl/PandaPush-v3/. To view these logs with TensorBoard, run the following command in this repository's root directory.

tensorboard --log-dir logs/rl/

Note: To concurrently log to Weights and Biases, adjust wandb.entity and wandb.project to your profile and set wandb.enabled=True (the default when omitting wandb=off).

Paper replication

As before, all configurations for experiments conducted in the paper are collected in configs/experiments/. To train, e.g., the "full state" RL agent, you can just run

python scripts/train_rl.py \
    +experiment=rl-full

To train an RL agent with SAE-encoded keypoints, e.g. with "Basic-kp32" (also see Naming), additionally specify a checkpoint:

python scripts/train_rl.py \
    +experiment=rl-feat-only \
    training.sae_checkpoint=logs/sae/panda_push_custom/basic+basic/YYYY-MM-DD--HH-MM-SS--0/checkpoint_final.pth \
    training.sae_name=random-name-42 \
    training.sae_experiment=sae-basic-fp32

More information

Documentation

All classes and functions have docstrings, specifying what they do.

Plotting

IPython notebooks with the source code for all plots can be found in notebooks/. Some of these plots require saved weights from logged runs. You can adjust the file paths accordingly to load your own checkpoints. Other plots require access to our private Weights and Biases project. You can insert your own entity and project there if you logged to Weights and Biases yourself.

Naming

The "-std" modification mentioned in the paper is called "-var" in this codebase. "Ground-truth points" in the paper are "keypoints" in this codebase and "keypoints" in the paper are "feature points" here.