Groupoid-Based Internal State Representations for Reinforcement Learning with Local Symmetries
Groupoid-based RL discovers local, state-dependent symmetries during interaction, learning in a symmetry-reduced space and beating standard Q-learning efficiency.
The paper proposes a reinforcement learning framework using groupoids to capture local, state-dependent symmetries and dynamically discover equivalence structures during interaction. The agent maintains orbit representatives together with transporters that map raw states to canonical forms, enabling learning and decision-making in a symmetry-reduced space while preserving local distinctions. Empirical results show improved sample efficiency and convergence over standard Q-learning in dense and large-scale environments with strong partial symmetries.
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