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

Source: arXiv cs.LG — read the full report at the original publisher.

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