dm_memorytasks: DeepMind Memory Task Suite

The DeepMind Memory Task Suite is a set of 13 diverse machine-learning tasks that require memory to solve. They are constructed to let us evaluate generalization performance on a memory-specific holdout set.

The 8 tasks in this repo are Unity-based. Besides these, there are 4 tasks in the overall Memory Task Suite that are modifications of PsychLab tasks, and 1 that is a modification of a DeepMind Lab level.

NOTE: The 5 other tasks in the Suite are in Psychlab and DeepMind Lab, not Unity. Psychlab is part of DeepMind Lab. DeepMind Lab has a separate set of installation instructions.

Overview

The 8 Unity-based tasks are provided through a pre-packaged Docker container.

The dm_memorytasks package consists of support code to run these Docker containers. You interact with the task environment via a dm_env Python interface.

Please see the documentation for more detailed information on the available tasks, actions and observations.

Requirements

dm_memorytasks requires Docker, Python 3.6.1 or later and a x86-64 CPU with SSE4.2 support. We do not attempt to maintain a working version for Python 2.

Note: We recommend using Python virtual environment to mitigate conflicts with your system's Python environment.

Download and install Docker:

• For Linux, install Docker-CE
• Install Docker Desktop for OSX or Windows.

Installation

dm_memorytasks can be installed from PyPi using pip:

$ pip install dm-memorytasks

To also install the dependencies for the examples/, install with:

$ pip install dm-memorytasks[examples]

Alternatively, you can install dm_memorytasks by cloning a local copy of our GitHub repository:

$ git clone https://github.com/deepmind/dm_memorytasks.git
$ pip install ./dm_memorytasks

Usage

Once dm_memorytasks is installed, to instantiate a dm_env instance run the following:

import dm_memorytasks

settings = dm_memorytasks.EnvironmentSettings(seed=123, level_name='spot_diff_train')
env = dm_memorytasks.load_from_docker(settings)

Rutgers ILabs

If you are running this code on the Rutgers Ilabs, you can run the dockerrun.bash script which will pull the container and set it up for you

Setting up python environment:

Recommend setup for conda environment: python=3.8 conda create -n myenv python=3.8

When you run human agent or random agent you get numpy error: Solve it by changing the np.object -> object

Install all the above dependency before install stable baseline

Install absl

conda install -c anaconda absl-py

Install gymnasium

conda install -c conda-forge gymnasium

Stable Baselines

To train the A2C agent, we use the framework provided by stable baselines 3 (https://stable-baselines3.readthedocs.io/en/master/modules/a2c.html), please install the library before you run either a2c_invis_builds.py or a2c_spot_diff.py.