OfflineMania: A Benchmark Environment for Offline Reinforcement Learning in Racing Games

Overview

Welcome to the OfflineMania repository! This novel environment for Offline Reinforcement Learning (ORL) research is inspired by the TrackMania series and developed using the Unity 3D game engine. It simulates a single-agent racing game with the objective of optimal track navigation, providing diverse datasets for ORL research.

Abstract

Offline RL (ORL) reduces the high sample complexity of traditional RL by eliminating the need for continuous environmental interactions, using a dataset of pre-collected transitions. This repository introduces OfflineMania, a new environment built using the Unity 3D game engine. It provides datasets of varying sizes and expertise levels, establishing a challenging testbed for ORL algorithm development and evaluation. We also provide baseline results for a range of Online RL, ORL, and hybrid Offline to Online RL approaches.

Environment Details

State Space

The state space includes 15 raycasts covering a 180-degree field of view in front of the car and the car's (X, Y, Z) velocity components.

Action Space

The action space consists of two continuous values:

• Steering angle: Ranges from -1 (left turn) to 1 (right turn).
• Acceleration/Braking: Ranges from -1 (braking/reversing) to 1 (full acceleration).

Reward Signal

The reward function is progress-based with penalties for collisions scaling with the velocity of the car.

$$ r_t = r_t^{\text{prog}} - \begin{cases} \lambda \parallel v_{\text{car}}\parallel & \text{if in contact with wall} \\ 0 & \text{otherwise} \end{cases} $$

Episode

Each episode has a fixed length of 2,000 steps with randomized start position and orientation to ensure variety in training scenarios.

Datasets

• Basic: 100,000 transitions from a policy struggling with the initial corner.
• Medium: 100,000 transitions from a policy occasionally completing the track.
• Expert: 100,000 transitions from a high-performance policy.
• Mix Large: 200,000 transitions, combining 90% basic, 7% medium, and 3% expert.
• Mix Small: 5,000 transitions with the same mix ratio as Mix Large.
• Basic Small: 5,000 transitions from the basic policy.

Installation

1. Clone the repository:

   git clone https://github.com/ganjiro/OfflineMania.git

2. Follow the official guide to install the Unity ML-Agents Toolkit.

3. Install the required dependencies:

   pip install -r requirements.txt

4. For Linux users only set the permission to run the game using ML-Agents:

   chmod -R 755 Game/Linux/OfflineMania.x86_64

Usage

The main.py script loads the selected dataset and launches an episode in the environment. Use the following commands to run the script:

Launching on Windows:

python main.py --game_file 'Game/Windows/OfflineMania.exe' --dataset_path 'OfflineMania/Datasets' --dataset_name 'mixLarge'

Launching on Linux:

python main.py --game_file 'Game/Linux/OfflineMania.x86_64' --dataset_path 'OfflineMania/Datasets' --dataset_name 'mixLarge'

Acknowledgements

Our environment is based on IR-Project. We thank the author for releasing his code.

Authors

• Girolamo Macaluso (University of Florence)
• Alessandro Sestini (SEED - Electronic Arts)
• Andrew D. Bagdanov (University of Florence)

Citation

@misc{macaluso2024benchmarkenvironmentofflinereinforcement,
      title={A Benchmark Environment for Offline Reinforcement Learning in Racing Games}, 
      author={Girolamo Macaluso and Alessandro Sestini and Andrew D. Bagdanov},
      year={2024},
      eprint={2407.09415},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2407.09415}, 
}

Contact

For any inquiries, please contact:

• Girolamo Macaluso: girolamo.macaluso@unifi.it