Amortized Feedback Planning: Turning Model-Based Rollouts into Executable Policies

arXiv:2609.35012 · math.OC, cs.SY, eess.SY · Submitted 2026-09-28 · Read on arXiv

math.OC, cs.SY, eess.SY

Submitted: 2026-09-28

Updated: 2026-09-28

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Actor — 29× in this paper · explained in 3 episodes
• Accuracy — 21× in this paper · explained in 1 episode
• Consistency — 7× in this paper · explained in 3 episodes
• Alignment — 5× in this paper · explained in 1 episode
• Distillation — 5× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Benchmark — 4× in this paper · explained in 2 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Core — 3× in this paper · explained in 2 episodes
• Frozen model — 2× in this paper · explained in 3 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• World Model — 2× in this paper · explained in 7 episodes
• Additive Decomposition — 1× in this paper · explained in 1 episode
• Approximation Bound — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Closed-Loop Optimization — 1× in this paper · explained in 2 episodes
• Error Decomposition — 1× in this paper · explained in 3 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• Local Optimization — 1× in this paper · explained in 2 episodes
• Provenance — 1× in this paper · explained in 3 episodes
• Regret — 1× in this paper · explained in 3 episodes
• System Identification — 1× in this paper · explained in 3 episodes
• Time Series Forecasting — 1× in this paper · explained in 3 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Dream to Control: Learning Behaviors by Latent Imagination
• TD-MPC2: Scalable, Robust World Models for Continuous Control
• Learning Continuous Control Policies by Stochastic Value Gradients

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