Environment Parameter Gradient Theorem for Co-Design in Reinforcement Learning

arXiv:2607.12590 · eess.SY, cs.LG, cs.SY · Submitted 2026-07-14 · Read on arXiv

eess.SY, cs.LG, cs.SY

Submitted: 2026-07-14

Updated: 2026-09-05

Comments: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

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

Terminology

• Actor — 15× in this paper · explained in 3 episodes
• Reinforcement Learning — 15× in this paper · explained in 4 episodes
• Decoupling — 3× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 3× in this paper · explained in 3 episodes
• Reinforcement Learning (RL) — 3× in this paper · explained in 25 episodes
• Action Space — 2× in this paper · explained in 1 episode
• Regularization — 2× in this paper · explained in 3 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Bayesian Optimization — 1× in this paper · explained in 3 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• IEEE International Conference on Robotics and Automation — 1× in this paper
• Loss Function — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Soft Actor-Critic (SAC) — 1× in this paper · explained in 2 episodes

Sources

• Taming the Noise in Reinforcement Learning via Soft Updates

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