NeurIPS 2021- AWS Deepracer AI Driving Olympics Challenge - Starter Kit

This repository is the AWS Deepracer AI Driving Olympics Challenge Submission template and Starter kit!

AWS DeepRacer is an AWS Machine Learning service for exploring reinforcement learning that is focused on autonomous racing. In this competition, you will train a reinforcement learning agent (i.e. an autonomous car), that learns to drive by interacting with its environment, e.g., the track, by taking an action in a given state to maximize the expected reward. Your goal is to train a model that can complete a lap as fast as possible without going off track, while avoiding crashing into the objects placed on the track.

Clone the repository to compete now!

This repository contains:

• Documentation on how to submit your agent to the leaderboard
• The procedure for best practices and information on how we evaluate your agent, etc.
• Starter code for you to get started!
• Baseline: Baseline Models

IMPORTANT - Accept the rules before you submit

Table of Contents

• 📚 Competition Procedure
• 💪 Setup
• 🛠️ Specify software runtime / dependencies?
• 🚀 Making a submission
• 🤔 Other concepts and FAQs
• 📎 Important links

📚 Competition Procedure

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

1. Sign up to join the competition [on the AIcrowd website].(https://www.aicrowd.com/challenges/neurips-2021-aws-deepracer-ai-driving-olympics-challenge)
2. Clone this repo and start developing your solution.
3. Train your models and writer code in run.py.
4. Submit your trained models to AIcrowd Gitlab for evaluation (full instructions below). The automated evaluation setup will evaluate the submissions against the test dataset to compute and report the metrics on the leaderboard of the competition.

💪 Setup

2. Clone the repository

   git clone git@gitlab.aicrowd.com:neurips-2021-aws-deepracer-ai-driving-olympics-challenge/neurips-2021-aws-deepracer-ai-driving-olympics-challenge-starter-kit.git

3. Install competition specific dependencies!

   cd neurips-2021-aws-deepracer-ai-driving-olympics-challenge-starter-kit
   pip3 install -r requirements.txt

4. Try out the baseline model available in run.py.

🚀 Making a submission

Repository structure

Please follow the example structure as it is 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                   # Your local dataset copy - you don't need to upload it (read DATASET.md)
├── requirements.txt       # Python packages to be installed
├── run.py                # IMPORTANT: Your testing/inference phase code, must be derived from AirbornePredictor (example in test.py)

Specify runtime/dependencies

We accept submissions with custom runtime, so you don't need to worry about which libraries or framework to pick from.

The configuration files typically include requirements.txt (pypi packages), environment.yml (conda environment), apt.txt (apt packages) or even your own Dockerfile.

You can check detailed information about the same in the 👉 RUNTIME.md file.

Submitting to aicrowd

• Add your SSH key to AIcrowd GitLab

You can add your SSH Keys to your GitLab account by going to your profile settings here. If you do not have SSH Keys, you will first need to generate one.

• Your repository should have an aicrowd.json file with following fields:

{
  "challenge_id": "evaluations-api-deepracer",
  "grader_id": "evaluations-api-deepracer",
  "authors": ["aicrowd-bot"],
  "tags": "change-me",
  "description": "Random agent for AWS Deep Racer",
}

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

• Follow the instructions in SUBMISSION.md to get your submission evaluated.

🤔 Other Concepts

Time constraints

You need to make sure that your model finishes evaluation in 1500 seconds, otherwise your evaluation will be marked failed.

Local evaluation

You can also test end to end evaluation on your own systems, by executing run.py.

Hardware used for evaluations

We use g4dn instances to run your evaluations.

📎 Important links

💪  Challenge Page: https://www.aicrowd.com/challenges/neurips-2021-aws-deepracer-ai-driving-olympics-challenge

🗣️  Discussion Forum: https://www.aicrowd.com/challengesneurips-2021-aws-deepracer-ai-driving-olympics-challengee/discussion

🏆  Leaderboard: https://www.aicrowd.com/challenges/neurips-2021-aws-deepracer-ai-driving-olympics-challenge/leaderboards