Sufficiency of Zeroth-Order Reward Shaping for Policy Gradient in Stabilization Control

arXiv:2609.34695 · cs.RO, cs.AI · Submitted 2026-09-28 · Read on arXiv

cs.RO, cs.AI

Submitted: 2026-09-28

Updated: 2026-09-28

Comments: 21 pages, 10 figures, including appendix

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

Terminology

• Reward Shaping — 34× in this paper · explained in 2 episodes
• Actor — 17× in this paper · explained in 3 episodes
• Reinforcement Learning — 16× in this paper · explained in 4 episodes
• Deep Reinforcement Learning — 7× in this paper · explained in 2 episodes
• Reward Function — 6× in this paper · explained in 2 episodes
• Lyapunov function — 5× in this paper · explained in 2 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Trace — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Reinforcement Learning (RL) — 2× in this paper · explained in 25 episodes
• Core — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Markov Decision Process (MDP) — 1× in this paper · explained in 11 episodes
• Non-Linear Program — 1× in this paper
• Regret — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Sim-to-Real Transfer — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• Reward Design with Language Models
• DrEureka: Language Model Guided Sim-To-Real Transfer
• Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning
• Proximal Policy Optimization Algorithms
• RoboCLIP: One Demonstration is Enough to Learn Robot Policies

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