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arXiv CS.AI·May 25·8 min read
Reflex: Reinforcement Learning with Reflection Symmetry Exploitation in State-Based Continuous Control
TL;DR
Reinforcement learning agents learn inefficiently because they don't exploit structural patterns in their environments. Reflex is a method that identifies and leverages reflection symmetry (mirror-image equivalences) in state-based continuous control tasks by integrating symmetry constraints into policy learning.
✦ Why It Matters
Engineers can reduce training time and computational cost by incorporating domain symmetries into RL algorithms for robotic control and continuous optimization tasks.
Key Takeaways
- Identify reflection symmetries in your control problem to apply Reflex for faster learning.
- Reflex integrates with existing algorithms (PPO, SAC) without requiring architectural changes.
- Use axial or bilateral reflection formalization to characterize your environment's symmetries.
- Expect improved sample efficiency and performance on continuous control benchmarks with symmetry.
Full Summary
Reinforcement learning (RL)—where agents learn optimal behavior through trial and error—suffers from poor sample efficiency, requiring many environment interactions to learn effectively. Prior work exploited group-invariant MDPs (mathematical structures where certain transformations preserve problem structure), but focused on image-based RL and rotational symmetry, leaving state-based continuous control and reflection symmetry unexplored.
Reflex formalizes two reflection types—axial reflection (mirroring across a line) and bilateral reflection (left-right symmetry)—and integrates them into policy learning through symmetry regularization. The method works with both on-policy algorithms (PPO) and off-policy algorithms (SAC).
Experiments on OpenAI Gym and DeepMind Control Suite benchmarks demonstrated superior performance and improved sample efficiency compared to standard baselines, with code made publicly available.