First commit

.gitignore

+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+scratch/test-envs/

FAQ.md

+# Frequently Asked Questions
+
+A non-exhaustive list of Frequently Asked Questions for the [Flatland Challenge](https://www.aicrowd.com/challenges/flatland-challenge).
+
+## How do I locally build a docker image out of my submission ?
+
+* Install Dependencies
+- **docker** : By following the instructions [here](https://docs.docker.com/install/linux/docker-ce/ubuntu/)
+- **aicrowd-repo2docker**
+
+```sh
+pip install aicrowd-repo2docker
+```
+
+* Build Image
+```
+sudo aicrowd-repo2docker --no-run \
+  --user-id 1001 \
+  --user-name aicrowd \
+  --image-name my-random-agent \
+  --debug .
+```
+
+# Author
+Sharada Mohanty <https://twitter.com/MeMohanty>
\ No newline at end of file

README.md

+![AIcrowd-Logo](https://raw.githubusercontent.com/AIcrowd/AIcrowd/master/app/assets/images/misc/aicrowd-horizontal.png)
+
+# Flatland Challenge Starter Kit
+
+[![gitter-badge](https://badges.gitter.im/AIcrowd-HQ/flatland-rl.png)](https://gitter.im/AIcrowd-HQ/flatland-rl)
+
+Instructions to make submissions to the [SBB CFF Flatland Challenge](https://www.aicrowd.com/challenges/flatland-challenge).
+
+Participants will have to submit their code, with packaging specifications, and the evaluator will automatically build a docker image and execute their agent against an arbitrary number of pre-generated flatland environments.
+
+### Setup
+
+- **docker** : By following the instructions [here](https://docs.docker.com/install/linux/docker-ce/ubuntu/)
+- **aicrowd-repo2docker**
+
+```sh
+pip install aicrowd-repo2docker
+```
+
+- **Anaconda** (By following instructions [here](https://www.anaconda.com/download)) At least version `4.5.11` is required to correctly populate `environment.yml`.
+- **flatland-rl** (By following instructions [here](http://flatland-rl-docs.s3-website.eu-central-1.amazonaws.com/readme.html))
+  **IMPORTANT** : Please note that you will need `flatland-rl` version `>=0.3.5` to be able to submit which you can update in your anaconda env by :
+
+```
+pip install -U flatland-rl
+```
+
+- **Your code specific dependencies**
+
+```sh
+# If say you want to install PyTorch
+conda install pytorch torchvision -c pytorch
+```
+
+### Clone repository
+
+```
+git clone git@github.com:AIcrowd/flatland-challenge-starter-kit.git
+cd flatland-challenge-starter-kit
+```
+
+### Test Submission Locally
+
+```
+cd flatland-challenge-starter-kit
+export IMAGE_NAME="flatland-random-agent"
+
+# Build docker image for your submission
+./debug_build.sh
+
+# In a separate tab : run redis server
+./run_redis.sh
+
+# In a separate tab : run local grader
+./run_local_grader.sh
+
+# In a separate tab :
+# Finally, run your agent locally by :
+./docker_run.sh
+```
+
+# How do I specify my software runtime ?
+
+The software runtime is specified by exporting your `conda` env to the root
+of your repository by doing :
+
+```
+conda env export --no-build > environment.yml
+```
+
+This `environment.yml` file will be used to recreate the `conda environment` inside the Docker container.
+This repository includes an example `environment.yml`
+
+# What should my code structure be like ?
+
+Please follow the structure documented in the included [run.py](https://github.com/AIcrowd/flatland-challenge-starter-kit/blob/master/run.py) to adapt
+your already existing code to the required structure for this round.
+
+## Important Concepts
+
+### Repository Structure
+
+- `aicrowd.json`
+  Each repository should have a `aicrowd.json` with the following content :
+
+```json
+{
+  "challenge_id": "flatland-challenge-2019",
+  "grader_id": "flatland-challenge-2019",
+  "authors": ["your-aicrowd-username"],
+  "description": "sample description about your awesome agent",
+  "license": "MIT",
+  "debug": false
+}
+```
+
+This 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.
+
+If you set `debug` to `true`, then the evaluation will run on a separate set of 20 environments, and the logs from your submitted code (if it fails), will be made available to you to help you debug.
+
+### Packaging of your software environment
+
+You can specify your software environment by using all the [available configuration options of repo2docker](https://repo2docker.readthedocs.io/en/latest/config_files.html). (But please remember to use [aicrowd-repo2docker](https://pypi.org/project/aicrowd-repo2docker/) to have GPU support)
+
+The recommended way is to use Anaconda configuration files using **environment.yml** files.
+
+```sh
+# The included environment.yml is generated by the command below, and you do not need to run it again
+# if you did not add any custom dependencies
+
+conda env export --no-build > environment.yml
+
+# Note the `--no-build` flag, which is important if you want your anaconda env to be replicable across all
+```
+
+### Debugging the packaged software environment
+
+If you have issues with your submission because of your software environment and dependencies, you can debug them, by first building the docker image, and then getting a shell inside the image by :
+
+```
+docker run --net=host -it $IMAGE_NAME /bin/bash
+```
+
+and then exploring to find the cause of the issue.
+
+### Code Entrypoint
+
+The evaluator will use `/home/aicrowd/run.sh` as the entrypoint, so please remember to have a `run.sh` at the root, which can instantitate any necessary environment variables, and also start executing your actual code. This repository includes a sample `run.sh` file.
+If you are using a Dockerfile to specify your software environment, please remember to create a `aicrowd` user, and place the entrypoint code at `run.sh`.
+
+## Submission
+
+To make a submission, you will have to create a private repository on [https://gitlab.aicrowd.com/](https://gitlab.aicrowd.com/).
+
+You will have to add your SSH Keys to your GitLab account by following the instructions [here](https://docs.gitlab.com/ee/gitlab-basics/create-your-ssh-keys.html).
+If you do not have SSH Keys, you will first need to [generate one](https://docs.gitlab.com/ee/ssh/README.html#generating-a-new-ssh-key-pair).
+
+Then you can create a submission by making a _tag push_ to your repository on [https://gitlab.aicrowd.com/](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 flatland-challenge-starter-kit
+# Add AIcrowd git remote endpoint
+git remote add aicrowd git@gitlab.aicrowd.com/:<YOUR_AICROWD_USER_NAME>/flatland-challenge-starter-kit.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>/flatland-challenge-starter-kit/issues](gitlab.aicrowd.com//<YOUR_AICROWD_USER_NAME>/flatland-challenge-starter-kit/issues)
+
+**NOTE**: Remember to update your username in the link above :wink:
+
+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 :wink: ) :
+![](https://i.imgur.com/Kc7M8zH.png)
+
+and if everything works out correctly, then you should be able to see the final scores like this :
+![](https://i.imgur.com/9RT2jFi.png)
+
+**Best of Luck** :tada: :tada:
+
+# Author
+
+Sharada Mohanty <https://twitter.com/MeMohanty>

aicrowd.json

+{
+  "challenge_id": "aicrowd_flatland_challenge_2019",
+  "grader_id": "aicrowd_flatland_challenge_2019",
+  "authors": ["mohanty"],
+  "description": "Random Agent for Flatland Challenge.",
+  "debug" : false
+}
+

apt.txt

+curl
+git
+vim
+ssh
+gcc
\ No newline at end of file

docker_run.sh

+#!/bin/bash
+
+
+if [ -e environ_secret.sh ]
+then
+    echo "Note: Gathering environment variables from environ_secret.sh"
+    source environ_secret.sh
+else
+    echo "Note: Gathering environment variables from environ.sh"
+    source environ.sh
+fi
+
+# Expected Env variables : in environ.sh
+sudo docker run \
+    --net=host \
+    -v ./scratch/test-envs:/flatland_envs:z \
+    -it ${IMAGE_NAME}:${IMAGE_TAG} \
+    /home/aicrowd/run.sh

environment.yml

+name: flatland-rl-test
+channels:
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1
+  - ca-certificates=2019.5.15
+  - certifi=2019.6.16
+  - libedit=3.1.20181209
+  - libffi=3.2.1
+  - libgcc-ng=9.1.0
+  - libstdcxx-ng=9.1.0
+  - ncurses=6.1
+  - openssl=1.1.1c
+  - pip=19.1.1
+  - python=3.6.8
+  - readline=7.0
+  - setuptools=41.0.1
+  - sqlite=3.29.0
+  - tk=8.6.8
+  - wheel=0.33.4
+  - xz=5.2.4
+  - zlib=1.2.11
+  - pip:
+    - atomicwrites==1.3.0
+    - attrs==19.1.0
+    - bleach==3.1.0
+    - cairocffi==1.0.2
+    - cairosvg==2.4.0
+    - cffi==1.12.3
+    - chardet==3.0.4
+    - click==7.0
+    - crowdai-api==0.1.21
+    - cssselect2==0.2.1
+    - cycler==0.10.0
+    - defusedxml==0.6.0
+    - docutils==0.15.1
+    - filelock==3.0.12
+    - flatland-rl==0.3.6
+    - idna==2.8
+    - importlib-metadata==0.19
+    - importlib-resources==1.0.2
+    - kiwisolver==1.1.0
+    - lxml==4.4.0
+    - matplotlib==3.1.1
+    - more-itertools==7.2.0
+    - msgpack==0.6.1
+    - msgpack-numpy==0.4.4.3
+    - numpy==1.17.0
+    - packaging==19.0
+    - pandas==0.25.0
+    - pillow==6.1.0
+    - pkginfo==1.5.0.1
+    - pluggy==0.12.0
+    - py==1.8.0
+    - pyarrow==0.14.1
+    - pycparser==2.19
+    - pygments==2.4.2
+    - pyparsing==2.4.1.1
+    - pytest==5.0.1
+    - pytest-runner==5.1
+    - python-dateutil==2.8.0
+    - python-gitlab==1.10.0
+    - pytz==2019.1
+    - readme-renderer==24.0
+    - recordtype==1.3
+    - redis==3.3.2
+    - requests==2.22.0
+    - requests-toolbelt==0.9.1
+    - screeninfo==0.4
+    - six==1.12.0
+    - svgutils==0.3.1
+    - timeout-decorator==0.4.1
+    - tinycss2==1.0.2
+    - toml==0.10.0
+    - tox==3.13.2
+    - tqdm==4.32.2
+    - twine==1.13.0
+    - urllib3==1.25.3
+    - ushlex==0.99.1
+    - virtualenv==16.7.2
+    - wcwidth==0.1.7
+    - webencodings==0.5.1
+    - xarray==0.12.3
+    - zipp==0.5.2
+prefix: /home/mohanty/anaconda3/envs/flatland-rl-test
+

run.py

+from flatland.evaluators.client import FlatlandRemoteClient
+from flatland.envs.observations import TreeObsForRailEnv
+from flatland.envs.predictions import ShortestPathPredictorForRailEnv
+import numpy as np
+
+
+
+#####################################################################
+# Instantiate a Remote Client
+#####################################################################
+remote_client = FlatlandRemoteClient()
+
+#####################################################################
+# Define your custom controller
+#
+# which can take an observation, and the number of agents and 
+# compute the necessary action for this step for all (or even some)
+# of the agents
+#####################################################################
+def my_controller(obs, number_of_agents):
+    _action = {}
+    for _idx range(number_of_agents):
+        _action[_idx] = np.random.randint(0, 5)
+    return _action
+
+#####################################################################
+# Instantiate your custom Observation Builder
+# 
+# You can build your own Observation Builder by following 
+# the example here : 
+# https://gitlab.aicrowd.com/flatland/flatland/blob/master/flatland/envs/observations.py#L14
+#####################################################################
+my_observation_builder = TreeObsForRailEnv(
+                                max_depth=3,
+                                predictor=ShortestPathPredictorForRailEnv()
+                            )
+
+#####################################################################
+# Main evaluation loop
+#
+# This iterates over an arbitrary number of env evaluations
+#####################################################################
+evaluation_number = 0
+while True:
+
+    evaluation_number += 1
+    # Switch to a new evaluation environemnt
+    # 
+    # a remote_client.env_create is similar to instantiating a 
+    # RailEnv and then doing a env.reset()
+    # hence it returns the first observation from the 
+    # env.reset()
+    # 
+    # You can also pass your custom observation_builder object
+    # to allow you to have as much control as you wish 
+    # over the observation of your choice.
+    observation = remote_client.env_create(
+                    obs_builder_object=my_observation_builder
+                )
+    if not observation:
+        #
+        # If the remote_client returns False on a `env_create` call,
+        # then it basically means that your agent has already been 
+        # evaluated on all the required evaluation environments,
+        # and hence its safe to break out of the main evaluation loop
+        break
+    
+    print("Evaluation Number : {}".format(evaluation_number))
+
+    #####################################################################
+    # Access to a local copy of the environment
+    # 
+    #####################################################################
+    # Note: You can access a local copy of the environment 
+    # by using : 
+    #       remote_client.env 
+    # 
+    # But please ensure to not make any changes (or perform any action) on 
+    # the local copy of the env, as then it will diverge from 
+    # the state of the remote copy of the env, and the observations and 
+    # rewards, etc will behave unexpectedly
+    # 
+    # You can however probe the local_env instance to get any information
+    # you need from the environment. It is a valid RailEnv instance.
+    local_env = remote_client.env
+    number_of_agents = len(local_env.agents)
+
+    # Now we enter into another infinite loop where we 
+    # compute the actions for all the individual steps in this episode
+    # until the episode is `done`
+    # 
+    # An episode is considered done when either all the agents have 
+    # reached their target destination
+    # or when the number of time steps has exceed max_time_steps, which 
+    # is defined by : 
+    #
+    # max_time_steps = int(1.5 * (env.width + env.height))
+    #
+    while True:
+        #####################################################################
+        # Evaluation of a single episode
+        #
+        #####################################################################
+        # Compute the action for this step by using the previously 
+        # defined controlle
+        action = my_controller(obs, number_of_agents)
+
+        # Perform the chosen action on the environment.
+        # The action gets applied to both the local and the remote copy 
+        # of the environment instance, and the observation is what is 
+        # returned by the local copy of the env, and the rewards, and done and info
+        # are returned by the remote copy of the env
+        observation, all_rewards, done, info = remote_client.env_step(action)
+        if done['__all__']:
+            print("Reward : ", sum(list(all_rewards.values())))
+            #
+            # When done['__all__'] == True, then the evaluation of this 
+            # particular Env instantiation is complete, and we can break out 
+            # of this loop, and move onto the next Env evaluation
+            break
+
+print("Evaluation of all environments complete...")
+########################################################################
+# Submit your Results
+# 
+# Please do not forget to include this call, as this triggers the 
+# final computation of the score statistics, video generation, etc
+# and is necesaary to have your submission marked as successfully evaluated
+########################################################################
+print(remote_client.submit())

run.sh

+#!/bin/bash
+
+python ./run.py