Amir Esterhuysen
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126
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2026
The Terminal Representation in Reinforcement Learning
The paper introduces the Terminal Representation (TR), a novel, lower-dimensional, and structurally distinct formulation for encoding reward-weighted trajectories in RL that bypasses the need for eigendecomposition while retaining the benefits of existing representations.
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