SURREAL is an open-source distributed reinforcement learning framework designed for researchers and practitioners in robotics and AI. It addresses the significant computational demands of modern RL algorithms by providing a scalable and flexible platform for parallelized environment simulation and learning, aiming to accelerate training and improve reproducibility.

How It Works

SURREAL employs a decoupled architecture separating experience generation from learning. Parallel "actors" interact with environments, generating vast amounts of data, which are then sent to a centralized buffer. This buffer can be configured as a FIFO queue for on-policy algorithms or a replay memory for off-policy methods. This design allows for massive parallelization across thousands of CPUs and hundreds of GPUs, while the centralized learner ensures efficient model updates and diverse exploration.

Quick Start & Requirements

• Installation: Instructions for laptop and Google Cloud Kubernetes Engine deployment are available.
• Prerequisites: Requires a Python environment. Specific hardware requirements depend on the scale of deployment.
• Documentation: https://surrealai.github.io/surreal/docs/

Highlighted Details

• Scales to thousands of CPUs and hundreds of GPUs for accelerated training.
• Unifies on-policy and off-policy learning through a single algorithmic formulation.
• Designed for reproducibility, replicating cluster hardware and software runtime setups.
• Includes the Surreal Robotics Suite for benchmarking and complex task solving.

Maintenance & Community

The project originates from the Stanford Vision and Learning Lab. Further community engagement details are not explicitly provided in the README.

Licensing & Compatibility

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

Limitations & Caveats

The framework is presented as a research project, and its maturity for production use or extensive community support is not detailed. Specific dependencies and setup complexity for large-scale deployments may require significant engineering effort.