Gym-UnrealCV provides a framework for visual reinforcement learning (RL) by integrating Unreal Engine environments with the OpenAI Gym interface. It targets researchers and practitioners in robotics and computer vision who need realistic, high-fidelity simulated environments for training RL agents, offering pre-built scenarios for tasks like object tracking and manipulation.

How It Works

The project leverages UnrealCV as a bridge between Unreal Engine and OpenAI Gym. UnrealCV enables communication with Unreal Engine, allowing for sensor data (like images) to be extracted and actions to be sent to the simulated agent. This integration creates custom Gym environments that expose realistic visual observations and physics-based interactions, facilitating the development and testing of RL algorithms in complex, visually rich settings.

Quick Start & Requirements

• Install via pip: pip install -e . after cloning the repository.
• Dependencies: OpenAI Gym, OpenCV (cv2), Matplotlib, NumPy.
• Environment Binaries: Must be downloaded separately using python load_env.py -e {ENV_NAME}.
• Recommended: Anaconda for environment management.
• Optional: Docker, Nvidia-Docker for containerized execution.
• Official Docs: https://github.com/zfw1226/gym-unrealcv

Highlighted Details

• Supports multi-agent RL and adversarial RL setups.
• Offers pre-defined environments for active object tracking and object searching.
• Includes example implementations for DQN and DDPG agents.
• Facilitates customization of existing or addition of new Unreal Engine environments.

Maintenance & Community

The project has been cited in multiple academic papers, indicating active research use. Contact information is provided via email.

Licensing & Compatibility

The repository does not explicitly state a license in the README. This requires further investigation for commercial use or integration into closed-source projects.

Limitations & Caveats

The project requires downloading separate Unreal Engine environment binaries, which can be substantial in size. The lack of a specified license in the README presents a significant adoption blocker for commercial applications.