HPO enables unbiased policy optimization in hybrid action spaces by mixing differentiable simulation gradients with score-function estimates, outperforming PPO as continuous dimensions increase.
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Proves that stabilizing switched linear systems with full-history controllers implies existence of memoryless homogeneous degree one controllers depending only on current state (and mode if applicable).
Geometric Pareto Control embeds Pareto solutions in a Lie group submanifold and navigates via Riemannian gradient flow to achieve 100% feasibility and low suboptimality in control tasks without retraining.
ES-VAE applies TSRVF representation on Kendall's shape manifold inside a VAE to generate and classify skeletal trajectories while removing rigid transformations and timing variability, showing gains over standard VAEs on gait scoring and NTU action recognition.
Using common random numbers in rollout simulations provably reduces variance in relative utility estimates when a rollout policy is invoked beyond some depth.
citing papers explorer
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Policy Optimization in Hybrid Discrete-Continuous Action Spaces via Mixed Gradients
HPO enables unbiased policy optimization in hybrid action spaces by mixing differentiable simulation gradients with score-function estimates, outperforming PPO as continuous dimensions increase.
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Feedback Stabilization of Switched Systems: Memory is not needed
Proves that stabilizing switched linear systems with full-history controllers implies existence of memoryless homogeneous degree one controllers depending only on current state (and mode if applicable).
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Geometric Pareto Control: Riemannian Gradient Flow of Energy Function via Lie Group Homotopy
Geometric Pareto Control embeds Pareto solutions in a Lie group submanifold and navigates via Riemannian gradient flow to achieve 100% feasibility and low suboptimality in control tasks without retraining.
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An Elastic Shape Variational Autoencoder for Skeleton Pose Trajectories
ES-VAE applies TSRVF representation on Kendall's shape manifold inside a VAE to generate and classify skeletal trajectories while removing rigid transformations and timing variability, showing gains over standard VAEs on gait scoring and NTU action recognition.
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Using Common Random Numbers for Simulation-based Planning with Rollouts
Using common random numbers in rollout simulations provably reduces variance in relative utility estimates when a rollout policy is invoked beyond some depth.