gymnasium-search-race 2.1.0

A reinforcement learning environment for the Search Race CG puzzle based on Gymnasium

Gymnasium Search Race

Gymnasium environments for the Search Race CodinGame optimization puzzle and Mad Pod Racing CodinGame bot programming game.

https://github.com/user-attachments/assets/1862b04b-9e33-4f55-a309-ad665a1db2f1

Action Space	Box([-1, 0], [1, 1], float64)
Observation Space	Box([0, 0, 0, 0, 0, 0, 0, -1, -1, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], float64)
import	gymnasium.make("gymnasium_search_race:gymnasium_search_race/SearchRace-v1")

Installation

To install gymnasium-search-race with pip, execute:

pip install gymnasium_search_race

From source:

git clone https://github.com/Quentin18/gymnasium-search-race
cd gymnasium-search-race/
pip install -e .

Environment

Action Space

The action is a ndarray with 2 continuous variables:

• The rotation angle between -18 and 18 degrees, normalized between -1 and 1.
• The thrust between 0 and 200, normalized between 0 and 1.

Observation Space

The observation is a ndarray of 10 continuous variables:

• 1 if the next checkpoint is the last one, 0 otherwise.
• The x and y coordinates of the next checkpoint.
• The x and y coordinates of the checkpoint after next checkpoint.
• The x and y coordinates of the car.
• The horizontal speed vx and vertical speed vy of the car.
• The facing angle of the car.

The values are normalized between 0 and 1, or -1 and 1 if negative values are allowed.

Reward

The goal is to visit all checkpoints as quickly as possible, as such the agent is penalised with a reward of -0.1 for each timestep. When a checkpoint is visited, the agent is awarded with a reward of 1000/total_checkpoints.

Starting State

The starting state is generated by choosing a random CodinGame test case.

Episode End

The episode ends if either of the following happens:

1. Termination: The car visit all checkpoints before the time is out.
2. Truncation: Episode length is greater than 600.

Arguments

• test_id: test case id to generate the checkpoints (see choices here). The default value is None which selects a test case randomly when the reset method is called.

import gymnasium as gym

gym.make("gymnasium_search_race:gymnasium_search_race/SearchRace-v1", test_id=1)

Version History

• v1: Add boolean to indicate if the next checkpoint is the last checkpoint in observation
• v0: Initial version

Discrete environment

The SearchRaceDiscrete environment is similar to the SearchRace environment except the action space is discrete.

import gymnasium as gym

gym.make("gymnasium_search_race:gymnasium_search_race/SearchRaceDiscrete-v1", test_id=1)

Action Space

There are 74 discrete actions corresponding to the combinations of angles from -18 to 18 degrees and thrust 0 and 200.

Version History

• v1: Add all angles in action space
• v0: Initial version

Mad Pod Racing

The MadPodRacing and MadPodRacingDiscrete environments can be used to train a runner for the Mad Pod Racing CodinGame bot programming game. They are similar to the SearchRace and SearchRaceDiscrete environments except the following differences:

• The maximum thrust value is 100 instead of 200.
• The maps are generated the same way Codingame generates them.
• The car position is rounded and not truncated.

import gymnasium as gym

gym.make("gymnasium_search_race:gymnasium_search_race/MadPodRacing-v0")
gym.make("gymnasium_search_race:gymnasium_search_race/MadPodRacingDiscrete-v0")

Usage

You can use RL Baselines3 Zoo to train and evaluate agents:

pip install rl_zoo3

Train an Agent

The hyperparameters are defined in hyperparams/ppo.yml.

To train a PPO agent for the Search Race game, execute:

python -m rl_zoo3.train \
  --algo ppo \
  --env gymnasium_search_race/SearchRace-v1 \
  --tensorboard-log logs \
  --eval-freq 20000 \
  --eval-episodes 10 \
  --gym-packages gymnasium_search_race \
  --conf-file hyperparams/ppo.yml \
  --progress

Enjoy a Trained Agent

To see a trained agent in action on random test cases, execute:

python -m rl_zoo3.enjoy \
  --algo ppo \
  --env gymnasium_search_race/SearchRace-v1 \
  --n-timesteps 1000 \
  --deterministic \
  --gym-packages gymnasium_search_race \
  --load-best \
  --progress

Run Test Cases

To run test cases with a trained agent, execute:

python -m scripts.run_test_cases \
  --path rl-trained-agents/ppo/gymnasium_search_race-SearchRace-v1_1/best_model.zip \
  --env gymnasium_search_race:gymnasium_search_race/SearchRace-v1 \
  --record-video \
  --record-metrics

Tests

To run tests, execute:

pytest

Citing

To cite the repository in publications:

@misc{gymnasium-search-race,
  author = {Quentin Deschamps},
  title = {Gymnasium Search Race},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/Quentin18/gymnasium-search-race}},
}

References

• Gymnasium
• RL Baselines3 Zoo
• Stable Baselines3
• CGSearchRace
• CSB-Runner-Arena
• Coders Strikes Back by Magus

Assets

• https://www.flaticon.com/free-icon/space-ship_751036
• https://www.flaticon.com/free-icon/space-ship_784925

Author

Quentin Deschamps