MarLo provides a high-level API for multi-agent reinforcement learning experiments within Minecraft, targeting researchers and developers in the RL space. It offers a standardized, RL-friendly environment by wrapping the Malmö platform and extending OpenAI's Gym framework, enabling generalizable solutions for procedurally generated single and multi-agent scenarios.

How It Works

MarLo builds upon the Malmö platform, which exposes Minecraft's actions, observations (including visual frames and game statistics), and data. MarLo acts as a wrapper, abstracting Malmö's complexities into a higher-level API that conforms to the OpenAI Gym interface. This standardization facilitates easier integration with existing RL frameworks and allows for more accessible scientific study of RL algorithms in diverse, dynamic Minecraft environments.

Quick Start & Requirements

• Install via pip install marlo.
• Requires a running Minecraft client accessible on port 10000.
• Official documentation: https://marlo.readthedocs.io/en/latest/

Highlighted Details

• Integrates with OpenAI Gym for standardized RL environment interaction.
• Supports procedurally generated, pseudo-random, and changing environments.
• Offers a variety of pre-defined environments like MazeRunner, CliffWalking, and CatchTheMob.
• Was utilized in the 2018 MarLo Challenge.

Maintenance & Community

The project is actively seeking maintainers. Community interaction can be facilitated via the crowdAI Discord/Slack channels.

Licensing & Compatibility

The repository does not explicitly state a license in the README. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The project README explicitly states that the library is actively looking for maintainers, indicating potential risks regarding ongoing development and support. The license is not specified, which may pose a barrier to commercial adoption.