Adaptive Smooth Tchebycheff Attention for Multi-Objective Policy Optimization

Multi-objective reinforcement learning in robotic domains requires balancing complex, non-convex trade-offs between conflicting objectives. While linear scalarization methods provide stability, they are theoretically incapable of recovering solutions within non-convex regions of the Pareto front. Conversely, static non-linear scalarizations (e.g., Tchebycheff) can theoretically access these regions but often suffer from severe gradient variance and optimization instability in deep RL. In this wo

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ethics.ai (12 May 2026), “Adaptive Smooth Tchebycheff Attention for Multi-Objective Policy Optimization,” evidence record 4430, https://ethics.ai/record/4430 (originally published by arXiv).

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