Reflex: Reinforcement Learning with Reflection Symmetry Exploitation in State-Based Continuous Control
arXiv:2605.23415v1 Announce Type: new Abstract: Reinforcement learning has long struggled with poor sample efficiency. One promising approach to mitigate this problem is leveraging group-invariant Markov Decision Processes ($G$-invariant MDPs). Existing works in this direction have primarily focused on image-based RL and rotational symmetry such as $\mathrm{SO(2)}$, leaving state-based RL and reflection symmetry largely underexplored. In this work, we focus on state-based continuous control tasks and exploit reflection symmetry by introducing Reflex, a paradigm that seamlessly integrates with
The paper leverages new research in reinforcement learning to address long-standing challenges in sample efficiency by exploiting reflection symmetry, expanding beyond prior work that focused on image-based and rotational symmetry.
Improving sample efficiency in reinforcement learning is crucial for developing more capable and less data-intensive AI systems, making advanced AI applications more feasible and widespread.
This research introduces a novel paradigm, Reflex, that integrates reflection symmetry into state-based continuous control, potentially reducing the computational and data requirements for training complex RL agents.
- · AI researchers
- · Robotics developers
- · Reinforcement learning platforms
- · Industries using autonomous control
- · AI methods reliant on high sample efficiency
- · Brute-force data collection strategies
More efficient training of AI agents for complex physical tasks will become possible.
This could accelerate the development and deployment of autonomous systems in diverse real-world applications, such as manufacturing and logistics.
Reduced resource requirements for AI training could lower the barrier to entry for AI development, fostering wider innovation and potentially decentralizing AI capabilities.
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