Environment Parameter Gradient Theorem for Co-Design in Reinforcement Learning
Reinforcement learning (RL) is traditionally concerned with learning a control policy for a fixed environment. In many engineering systems, however, the environment itself is alterable, i.e., physical or operational parameters can be tuned to shape the system's transition dynamics and costs experienced by the RL agent. This motivates jointly optimizing both the policy and the environment design parameters. To this end, we establish an Environment Parameter Gradient Theorem --- a formal expression for the gradient of the RL's objective function with respect to environment parameters. The key theoretical device is a generalized action-value function $Q_{π,ξ}(s,a,ζ)$, which comprises two cop...
| Authors | Amber Srivastava |
|---|---|
| Primary category | eess.SY |
| Published | 14 July 2026 |
| Last updated at source | 5 September 2026 |
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