Reflex: Reinforcement Learning with Reflection Symmetry Exploitation in State-Based Continuous Control

Researchers introduce Reflex, a reinforcement learning framework that exploits reflection symmetry in state-based continuous control tasks to improve sample efficiency. The method integrates with both on-policy (PPO) and off-policy (SAC) algorithms and demonstrates superior performance on standard benchmarks compared to baseline approaches.

Reflex addresses a fundamental challenge in reinforcement learning: sample inefficiency during training. While prior research leveraged group-invariant MDPs for image-based tasks using rotational symmetry, this work extends the paradigm to state-based continuous control by formalizing and exploiting reflection symmetry—both axial and bilateral. This represents a meaningful expansion of symmetry exploitation techniques beyond rotational patterns, filling a gap in the literature.

The research builds on established principles of symmetry-preserving optimal value functions and policies, translating theoretical insights into practical algorithms. By integrating symmetry regularization mechanisms directly into policy learning, Reflex achieves measurable improvements in both sample efficiency and final performance across OpenAI Gym and DeepMind Control benchmarks. The framework's compatibility with both on-policy and off-policy algorithms demonstrates broad applicability.

For the AI and robotics communities, improved sample efficiency has substantial practical implications. Training robotic systems and continuous control agents currently demands extensive data collection and computational resources. Methods that reduce these requirements accelerate deployment timelines and lower development costs. The open-source code release amplifies potential adoption across research institutions and industry applications.

Looking ahead, the next phase involves testing Reflex on increasingly complex real-world robotic tasks and exploring whether other symmetry types remain unexploited. Integration with modern model-based RL approaches and scaling to higher-dimensional state spaces could further validate the framework's utility. The work establishes a foundation for systematic symmetry exploitation in control problems, potentially inspiring similar approaches in other domains.

• →Reflex exploits reflection symmetry in state-based RL to improve sample efficiency and performance on continuous control tasks.
• →The framework integrates seamlessly with both on-policy (PPO) and off-policy (SAC) algorithms, enabling broad applicability.
• →Formalization of axial and bilateral reflection symmetries extends symmetry exploitation beyond previously studied rotational patterns.
• →Benchmark results demonstrate superior performance compared to standard baselines across OpenAI Gym and DeepMind Control suites.
• →Reduced sample requirements from symmetry exploitation lower computational costs and accelerate deployment of AI-driven control systems.