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 work, we propose an Adaptive Smooth Tchebycheff framework that resolves this tension by dynamically modulating the curvature of the optimization landscape. We introduce a novel conflict-driven contro...
| Authors | Alejandro Murillo-Gonzalez, Mahmoud Ali, Lantao Liu |
|---|---|
| Primary category | cs.RO |
| Published | 12 May 2026 |
| Last updated at source | 12 May 2026 |
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