AINeutralarXiv – CS AI · 6h ago6/10
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The Terminal Representation in Reinforcement Learning
Researchers introduce the Terminal Representation (TR), a novel approach to representation learning in reinforcement learning that encodes reward-weighted trajectories more efficiently than existing methods. The TR achieves comparable performance to established approaches like the Default Representation while reducing computational overhead and eliminating assumptions about symmetric transition dynamics.