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The submission template for the MineRL Competition on Sample Efficicent RL @ NeurIPS 2019. Clone this to make a new submission!
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README.md

NeurIPS 2019: MineRL Competition Starter Kit

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This repository is the main MineRL Competition submission template and starter kit! Compete to solve MineRLObtainDiamond-v0 now!

This repository contains:

  • Documentation on how to submit your agent to the leaderboard
  • The procedure for Round 1 (how long you should train your agent, how we evaluate and re-train your agent, etc.)
  • Starter code for you to base your submission!

Other Resources:

Competition Procedure: Round 1

Welcome to Round 1! The main task of the competition is solving the MineRLObtainDiamond-v0 environment. In this environment, the agent begins in a random starting location without any items, and is tasked with obtaining a diamond. This task can only be accomplished by navigating the complex item hierarchy of Minecraft.

In this round you will train your agents locally with a limited number of samples and then upload them to AIcrowd (via git) to be evaluated and retrained by the organizers!

The following is a high level description of how this round works

  1. Sign up to join the competition on the AIcrowd website.

  2. Clone this repo and start developing your submissions.

  3. Train your models against MineRLObtainDiamond-v0 using the train_locally.sh or on Azure with only 8,000,000 samples in less than four days using hardware no powerful than a NG6v2 instance (6 CPU cores, 112 GiB RAM, 736 GiB SDD, and a single NVIDIA P100 GPU.)

  4. Submit your trained models to AIcrowd Gitlab for evaluation (full instructions below). The automated evaluation setup will evaluate the submissions against the validation environment, to compute and report the metrics on the leaderboard of the competition.

Once Round 1 is complete, the organizers will:

  1. Examine the code repositories of the top submissions on the leaderboard to ensure compliance with the competition rules.

  2. Retrain the top submissions from scratch to ensure reproducibility of the leaderboard score! NOTE: Make sure that you train your models in UNDER 8,000,000 samples using a similar (or worse) hardware spec than above so that you are not disqualified for a score mismatch!

  3. Evaluate the resulting models again over several hundred episodes to determine the final ranking.

The code repositories associated with the corresponding submissions will be forked and scrubbed of any files larger than 15MB to ensure that participants are not using any pre-trained models in the subsequent round.

How to Submit a Model!

Setup

  1. Clone the github repository or press the "Use this Template" button on GitHub!

    git clone https://github.com/minerllabs/competition_submission_starter_template.git
    
  2. Install competition specific dependencies! Make sure you have the JDK 8 installed first!

    # 1. Make sure to install the JDK first
    # -> Go to http://minerl.io/docs/tutorials/getting_started.html
    
    # 2. Install the `minerl` package and the dependencies for the competition
    cd competition_submission_starter_template
    pip3 install -r requirements.txt
    
  3. Specify your specific submission dependencies (PyTorch, Tensorflow, kittens, etc.)

    • (Optional) Anaconda Environment. If you would like to use anaconda to manage your environment, make sure at least version 4.5.11 is required to correctly populate environment.yml (By following instructions here). Then:

      • Create your new conda environment

        conda create --name minerl_challenge
        conda activate minerl_challenge
      • Your code specific dependencies

        conda install <your-package>
    • Pip Packages If you are using specific Python packages make sure to add them to requirements.txt! Here's an example:

      # requirements.txt
      minerl>0.2.3
      
      matplotlib
      tensorflow
      
    • Apt Packages If your training procedure or agent depends on specific Debian (Ubuntu, etc.) packages, add them to apt.txt.

How do I specify my software runtime ?

As mentioned above, the software runtime is specified in 3 places:

  • environment.yml -- The optional Anaconda environment specification. As you add new requirements you can export your conda environment to this file!

    conda env export --no-build > environment.yml
    
  • requirements.txt -- The pip3 packages used by your agent to train. Note that dependencies specified by environment.yml take precedence over requirements.txt. As you add new pip3 packages to your training procedure either manually add them to requirements.txt or if your software runtime is simple, perform:

    # Put ALL of the current pip3 packages on your system in the submission
    pip3 freeze > requirements.txt
    
  • apt.txt -- The Debian packages (via aptitude) used by your training procedure!

These files are used to construct both the local and AICrowd docker containers in which your agent will train.

What should my code structure be like ?

Please follow the example structure shared in the starter kit for the code structure. The different files and directories have following meaning:

.
├── aicrowd.json           # Submission meta information like your username
├── apt.txt                # Packages to be installed inside docker image
├── data                   # The downloaded data, the path to directory is also available as `MINERL_DATA_ROOT` env variable
├── requirements.txt       # Python packages to be installed
├── run.sh                 # The default entry point which will execute your submission
├── test.py                # IMPORTANT: Your testing/inference phase code
├── train                  # Your trained model MUST be saved inside this directory
├── train.py               # IMPORTANT: Your training phase code
└── utility                # The utility scripts to provide smoother experience to you.
    ├── debug_build.sh
    ├── docker_run.sh
    ├── environ.sh
    ├── evaluation_locally.sh
    ├── parser.py
    ├── train_locally.sh
    └── verify_or_download_data.sh

Finally, you must specify an AIcrowd submission JSON in aicrowd.json to be scored!

The aicrowd.json of each submission should contain the following content:

{
  "challenge_id": "aicrowd-neurips-2019-minerl-challenge",
  "grader_id": "aicrowd-neurips-2019-minerl-challenge",
  "authors": ["your-aicrowd-username"],
  "description": "sample description about your awesome agent",
  "license": "MIT",
  "gpu": true
}

This JSON is used to map your submission to the said challenge, so please remember to use the correct challenge_id and grader_id as specified above.

Please specify if your code will use a GPU or not for the evaluation of your model. If you specify true for the GPU, a NVIDIA Tesla K80 GPU will be provided and used for the evaluation.

Dataset location

You don't need to upload the data set in submission and it will be provided in online submissions at MINERL_DATA_ROOT path. For local training and evaluations, you can download it once in your system via python /utility/verify_or_download_data.py or place manually into data/ folder.

Training and Testing Code Entrypoint (where you write your code!)

The evaluator will use train.py and test.py as the entrypoint for training and testing/inference stage respectively, so please remember to include the files in your submission!

The inline documentation in these files will guide you in interfacing with evaluator properly.

IMPORTANT: Saving Models during Training!

Before you sbumit make sure that your code does the following.

  • During training (train.py) save your models to the train/ folder.
  • During testing (test.py) load your model from the train/ folder.

It is absolutely imperative that you save your models during training (train.py) so that they can be used in the evaluation phase (test.py) on AICrowd, and so the oraganizers can retrain your models from scratch at the end of Round 1 and during Round 2!

How to submit a trained agent!

To make a submission, you will have to create a private repository on https://gitlab.aicrowd.com/.

You will have to add your SSH Keys to your GitLab account by following the instructions here. If you do not have SSH Keys, you will first need to generate one.

Then you can create a submission by making a tag push to your repository on https://gitlab.aicrowd.com/. Any tag push (where the tag name begins with "submission-") to your private repository is considered as a submission
Then you can add the correct git remote, and finally submit by doing :

cd competition_submission_starter_template
# Add AIcrowd git remote endpoint
git remote add aicrowd git@gitlab.aicrowd.com:<YOUR_AICROWD_USER_NAME>/competition_submission_starter_template.git
git push aicrowd master

# Create a tag for your submission and push
git tag -am "submission-v0.1" submission-v0.1
git push aicrowd master
git push aicrowd submission-v0.1

# Note : If the contents of your repository (latest commit hash) does not change,
# then pushing a new tag will **not** trigger a new evaluation.

You now should be able to see the details of your submission at : gitlab.aicrowd.com/<YOUR_AICROWD_USER_NAME>/competition_submission_starter_template/issues

NOTE: Remember to update your username in the link above 😉

In the link above, you should start seeing something like this take shape (each of the steps can take a bit of time, so please be patient too 😉 ) :

and if everything works out correctly, then you should be able to see the final scores like this :

Best of Luck 🎉 🎉

Other Concepts

Time constraints

Round 1

You have to train your models locally with under 8,000,000 samples and with worse or comprable hardware to that above and upload the trained model in train/ directory. But, to make sure, your training code is compatible with further round's interface, the training code will be executed in this round as well. The constraints will be timeout of 5 minutes.

Round 2

You are expected to train your model online using the training phase docker container and output the trained model in train/ directory. You need to ensure that your submission is trained in under 8,000,000 samples and within 4 days period. Otherwise, the container will be killed

Local evaluation

You can perform local training and evaluation using utility scripts shared in this directory. To mimic the online training phase you can run ./utility/train_locally.sh from repository root, you can specify --verbose for complete logs.

aicrowd_minerl_starter_kit❯ ./utility/train_locally.sh --verbose
2019-07-22 07:58:38 root[77310] INFO Training Start...
2019-07-22 07:58:38 crowdai_api.events[77310] DEBUG Registering crowdAI API Event : CROWDAI_EVENT_INFO training_started {'event_type': 'minerl_challenge:training_started'} # with_oracle? : False
2019-07-22 07:58:40 minerl.env.malmo.instance.17c149[77310] INFO Starting Minecraft process: ['/var/folders/82/wsds_18s5dq321scc1j531m40000gn/T/tmpnyzpjrsc/Minecraft/launchClient.sh', '-port', '9001', '-env', '-runDir', '/var/folders/82/wsds_18s5dq321scc1j531m40000gn/T/tmpnyzpjrsc/Minecraft/run']
2019-07-22 07:58:40 minerl.env.malmo.instance.17c149[77310] INFO Starting process watcher for process 77322 @ localhost:9001
2019-07-22 07:58:48 minerl.env.malmo.instance.17c149[77310] DEBUG This mapping 'snapshot_20161220' was designed for MC 1.11! Use at your own peril.
2019-07-22 07:58:48 minerl.env.malmo.instance.17c149[77310] DEBUG #################################################
2019-07-22 07:58:48 minerl.env.malmo.instance.17c149[77310] DEBUG          ForgeGradle 2.2-SNAPSHOT-3966cea
2019-07-22 07:58:48 minerl.env.malmo.instance.17c149[77310] DEBUG   https://github.com/MinecraftForge/ForgeGradle
2019-07-22 07:58:48 minerl.env.malmo.instance.17c149[77310] DEBUG #################################################
2019-07-22 07:58:48 minerl.env.malmo.instance.17c149[77310] DEBUG                Powered by MCP unknown
2019-07-22 07:58:48 minerl.env.malmo.instance.17c149[77310] DEBUG              http://modcoderpack.com
2019-07-22 07:58:48 minerl.env.malmo.instance.17c149[77310] DEBUG          by: Searge, ProfMobius, Fesh0r,
2019-07-22 07:58:48 minerl.env.malmo.instance.17c149[77310] DEBUG          R4wk, ZeuX, IngisKahn, bspkrs
2019-07-22 07:58:48 minerl.env.malmo.instance.17c149[77310] DEBUG #################################################
2019-07-22 07:58:48 minerl.env.malmo.instance.17c149[77310] DEBUG Found AccessTransformer: malmomod_at.cfg
2019-07-22 07:58:49 minerl.env.malmo.instance.17c149[77310] DEBUG :deobfCompileDummyTask
2019-07-22 07:58:49 minerl.env.malmo.instance.17c149[77310] DEBUG :deobfProvidedDummyTask
...

For local evaluation of your code, you can use ./utility/evaluation_locally.sh, add --verbose if you want to view complete logs.

aicrowd_minerl_starter_kit❯ ./utility/evaluation_locally.sh
{'state': 'RUNNING', 'score': {'score': 0.0, 'score_secondary': 0.0}, 'instances': {'1': {'totalNumberSteps': 1001, 'totalNumberEpisodes': 0, 'currentEnvironment': 'MineRLObtainDiamond-v0', 'state': 'IN_PROGRESS', 'episodes': [{'numTicks': 1001, 'environment': 'MineRLObtainDiamond-v0', 'rewards': 0.0, 'state': 'IN_PROGRESS'}], 'score': {'score': 0.0, 'score_secondary': 0.0}}}}
{'state': 'RUNNING', 'score': {'score': 0.0, 'score_secondary': 0.0}, 'instances': {'1': {'totalNumberSteps': 2001, 'totalNumberEpisodes': 0, 'currentEnvironment': 'MineRLObtainDiamond-v0', 'state': 'IN_PROGRESS', 'episodes': [{'numTicks': 2001, 'environment': 'MineRLObtainDiamond-v0', 'rewards': 0.0, 'state': 'IN_PROGRESS'}], 'score': {'score': 0.0, 'score_secondary': 0.0}}}}
{'state': 'RUNNING', 'score': {'score': 0.0, 'score_secondary': 0.0}, 'instances': {'1': {'totalNumberSteps': 3001, 'totalNumberEpisodes': 0, 'currentEnvironment': 'MineRLObtainDiamond-v0', 'state': 'IN_PROGRESS', 'episodes': [{'numTicks': 3001, 'environment': 'MineRLObtainDiamond-v0', 'rewards': 0.0, 'state': 'IN_PROGRESS'}], 'score': {'score': 0.0, 'score_secondary': 0.0}}}}
{'state': 'RUNNING', 'score': {'score': 0.0, 'score_secondary': 0.0}, 'instances': {'1': {'totalNumberSteps': 4001, 'totalNumberEpisodes': 0, 'currentEnvironment': 'MineRLObtainDiamond-v0', 'state': 'IN_PROGRESS', 'episodes': [{'numTicks': 4001, 'environment': 'MineRLObtainDiamond-v0', 'rewards': 0.0, 'state': 'IN_PROGRESS'}], 'score': {'score': 0.0, 'score_secondary': 0.0}}}}
{'state': 'RUNNING', 'score': {'score': 0.0, 'score_secondary': 0.0}, 'instances': {'1': {'totalNumberSteps': 5001, 'totalNumberEpisodes': 0, 'currentEnvironment': 'MineRLObtainDiamond-v0', 'state': 'IN_PROGRESS', 'episodes': [{'numTicks': 5001, 'environment': 'MineRLObtainDiamond-v0', 'rewards': 0.0, 'state': 'IN_PROGRESS'}], 'score': {'score': 0.0, 'score_secondary': 0.0}}}}
{'state': 'RUNNING', 'score': {'score': 0.0, 'score_secondary': 0.0}, 'instances': {'1': {'totalNumberSteps': 6001, 'totalNumberEpisodes': 0, 'currentEnvironment': 'MineRLObtainDiamond-v0', 'state': 'IN_PROGRESS', 'episodes': [{'numTicks': 6001, 'environment': 'MineRLObtainDiamond-v0', 'rewards': 0.0, 'state': 'IN_PROGRESS'}], 'score': {'score': 0.0, 'score_secondary': 0.0}}}}
...

For running/testing your submission in a docker environment (ideantical to online submission), you can use ./utility/docker_train_locally.sh and ./utility/docker_evaluation_locally.sh. You can also run docker image with bash entrypoint for debugging on the go with the help of ./utility/docker_run.sh. These scripts respect following parameters:

  • --no-build: To skip docker image build and use the last build image
  • --nvidia: To use nvidia-docker instead of docker which include your nvidia related drivers inside docker image

Team

The quick-start kit was authored by Shivam Khandelwal with help from William H. Guss

The competition is organized by the following team:

  • William H. Guss (Carnegie Mellon University)
  • Mario Ynocente Castro (Preferred Networks)
  • Cayden Codel (Carnegie Mellon University)
  • Katja Hofmann (Microsoft Research)
  • Brandon Houghton (Carnegie Mellon University)
  • Noboru Kuno (Microsoft Research)
  • Crissman Loomis (Preferred Networks)
  • Keisuke Nakata (Preferred Networks)
  • Stephanie Milani (University of Maryland, Baltimore County and Carnegie Mellon University)
  • Sharada Mohanty (AIcrowd)
  • Diego Perez Liebana (Queen Mary University of London)
  • Ruslan Salakhutdinov (Carnegie Mellon University)
  • Shinya Shiroshita (Preferred Networks)
  • Nicholay Topin (Carnegie Mellon University)
  • Avinash Ummadisingu (Preferred Networks)
  • Manuela Veloso (Carnegie Mellon University)
  • Phillip Wang (Carnegie Mellon University)

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