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In order to participate in the competition you will need to upload a docker image containing your trained agent that interfaces with the animalai library. We detail the steps for participating below.

Python agent and associated data

Submissions need to implement the agent script provided. This script must implement the methods present in the base script and keep the same class name. The methods are:

  • __init__: this will only be called once when the agent is loaded first. It can contain loading of the model and other related parameters.
  • reset(t): will be called each time the arena resets. At test time the length of episodes will vary across the 300 experiments we will run, therefore we provide the agent with the length of the episode to come. Test lengths are either 250, 500, or 1000.
  • step(obs, reward, done, info): the method that is called each time the agent has to take a step. The arguments are the ones returned by the Gym environment AnimalAIEnv from animalai.envs.environment. If you wish to directly work on the ML Agents BrainInfo you can access them via info['brain_info']

Make sure any data loaded in the docker is referred to using absolute paths in the container or the form /aaio/data/... (see below). An example that you can modify is provided here

Create an EvalAI account and add submission details

The competition is kindly hosted by EvalAI. Head over to their website, create an account, and enroll your team in the AnimalAI challenge. To be able to submit and be eligible for prizes you will also need to register your personal details using this form.

Any question related solely to the submission process on EvalAI should be posted to the EvalAI forum


Docker offers a containerized platform for running applications in a closed environment. You can install all the libraries your agent will require and we will use this to run the tests as they would run on your local machine. The hardware we're using to run the tests is an AWS p2.xlarge instance.

Take the time to read the Docker documentation and follow the install process.

Adding CUDA capabilities to Docker (optional)

As part of the evaluation we offer GPU compute on an AWS p2.xlarge instance. These compute instances will run an Amazon Deep Learning Base AMI with several CUDA libraries installed.

The native docker engine does not provide a pass-through to these libraries, rendering any use of GPU capable libraries (such as tensorflow-gpu) impossible. To overcome this issue, NVIDIA provides a specific version of docker. We can recommend this tutorial for installing this version. Note we cannot provide help with installing these.

Creating the docker image for submission

Once you have docker up and running, you can start building your submission. Head over to examples/sumission and have a look at the Dockerfile. This script installs all the requirements for the environment, we do not recommend editing anything outside of the commented block saying YOUR COMMANDS GO HERE.

If your submission only requires the animalai-train library to run, you can use Dockerfile without any modification. While in examples/submission run:

docker build --tag=submission .

You can give your docker the name you want, it does not have to be submission. Note that the Dockerfile creates two folders /aaio and /aaio/data at the root of the image, and copies the file and data folder from your local machine into the image. Your submission must keep this architecture. References to these folders in your code should use absolute paths (see the example agent provided in examples/submission).

Test your docker image

As uploading and evaluating images takes a while, and you are only allowed a maximum of one submission per day, it is recommended to ensure your docker runs properly before submitting. If there is a failure during testing you will only have access to abridged outputs which may not be enough to debug on your own. If you cannot find a solution using the provided submission testing volume you will need to raise a question on the forum and we will investigate for you (which might take time).

Bottom line: be sure to test your submission prior to uploading!

First, copy the AnimalAI linux environment (and AnimalAI_Data folder) to examples/submission/test_submission/env.

Next, you need to run the image by mounting the test_submission folder and its content as a volume, and execute the script. To do so, from the submission folder, run:

docker run -v "$PWD"/test_submission:/aaio/test submission python /aaio/test/ 

If your image and agent are set properly, you should not get any error, and the script should output the rewards for 5 simple tests and conclude with SUCCESS

Submit your docker image

You can now submit your image to EvalAI for evaluation as explained on the EvalAI submission page.

Note: the phase name to use when pushing is: animalai-main-396. To push your image use evalai push <image>:<tag> --phase animalai-main-396 (details are at the bottom of the EvalAI page linked above).

Docker evaluation and results

On the EvalAI page you will see that the number of valid submissions is limited to one a day. A submission is valid if it fulfils the following requirements:

  • it does not crash at any point before the first two experiments are complete (this includes loading the agent, resetting it, and completing the two experiments)
  • loading the agent takes less than 5 minutes
  • running the first two experiments takes less than 10 minutes

If your submission meets these requirements it will be flagged as valid and you will not be able to submit again until the following day.

Completing the experiments cannot take longer than 80 minutes in total. If your submission goes over the time limit it will stop and you will score for any experiments that were completed.

Example scenarios:

  • FAIL: agent loads in 2 minutes, crashes during test number 2
  • FAIL: agent loads in 1 minute, takes more than 10 minutes to complete tests 1 and 2
  • SUCCESS: your agent loads in 3 minutes, takes 30 seconds for test 1, takes 1 minute for test two, it therefore has 78.5 minutes to complete the remaining 298 experiments
  • SUCCESS: agent loads in 4 minutes, completes test 1 and 2 in 1 minute, uses all the 79 minutes remaining to complete only 100 tests, you will get results based on the 102 experiments ran
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