Introduction

OGym is a Rust-native reinforcement learning framework inspired by OpenAI Gym. It provides a flexible, type-safe, and efficient platform for building, training, and interacting with reinforcement learning environments.

Built entirely in Rust, OGym uses ownership and zero-cost abstractions to provide thread safety and strong performance, particularly for parallel environment execution.

Performance

Features

• Type safety first: Strongly typed actions and observations eliminate common runtime shape errors.
• High performance: Compiled environments run with minimal overhead.
• Native vectorization: Batched environments support parallel processing and matrix operations.
• Modular backends: The architecture supports classic control systems and multiple physics engines, including Rapier and MuJoCo.
• Zero-cost abstractions: Idiomatic Rust patterns provide safety without sacrificing performance.