Learning in Markov Decision Processes with Exogenous Dynamics
Reinforcement learning algorithms are typically designed for generic Markov Decision Processes (MDPs), where any state-action pair can lead to an arbitrary transition distribution. In many practical systems, however, only a subset of the state variables is directly influenced by the agent's actions, while the remaining components evolve according to exogenous dynamics and account for most of the stochasticity. In this work, we study a structured class of MDPs characterized by exogenous state components whose transitions are independent of the agent's actions. We show that exploiting this structure yields significantly improved learning guarantees, with only the size of the exogenous state...
| Authors | Davide Maran, Davide Salaorni, Marcello Restelli |
|---|---|
| Primary category | cs.LG |
| Published | 3 March 2026 |
| Last updated at source | 4 March 2026 |
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