Adaptive Agent Design
We consider an agent acting against a general non-Markovian environment. The agent maintains its agent states, but is free to choose a transition kernel across those states and optimize its state-feedback control policies. We study the bi-level agent design problem that optimizes the transition kernel and the policy it induces, given said kernel with offline data of observations and actions obtained via a behavioral policy. For general environments, we show that a soft $Q$-learning algorithm converges almost surely to the fixed point of a soft Bellman equation defined by the stationary averages that the behavioral policy and the chosen kernel induce, and we delineate what separates the re...
| Authors | Raj Kiriti Velicheti, Subhonmesh Bose, Tamer Başar |
|---|---|
| Primary category | eess.SY |
| Published | 11 September 2026 |
| Last updated at source | 11 September 2026 |
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