SURF derives weight sampling rules from the arc-length CDF of the scalarization path to uniformly traverse the Pareto front in multi-objective optimization.
A theory of regularized markov decision processes
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A single preference-conditioned policy achieves unique and Lipschitz-continuous Pareto coverage in multi-objective MDPs via a new mirror-descent policy iteration algorithm with O(1/k) convergence.
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SURF: Steering the Scalarization Weight to Uniformly Traverse the Pareto Front
SURF derives weight sampling rules from the arc-length CDF of the scalarization path to uniformly traverse the Pareto front in multi-objective optimization.
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A Single Deep Preference-Conditioned Policy for Learning Pareto Coverage Sets
A single preference-conditioned policy achieves unique and Lipschitz-continuous Pareto coverage in multi-objective MDPs via a new mirror-descent policy iteration algorithm with O(1/k) convergence.