Joint MDPs and Reinforcement Learning in Coupled-Dynamics Environments
Many distributional quantities in reinforcement learning are intrinsically joint across actions, including distributions of gaps and probabilities of superiority. However, the classical Markov decision process (MDP) formalism specifies only marginal laws and leaves the joint law of counterfactual one-step outcomes across multiple possible actions at a state unspecified. We study coupled-dynamics environments with a multi-action generative interface which can sample counterfactual one-step outcomes for multiple actions under shared exogenous randomness. We propose joint MDPs (JMDPs) as a formalism for such environments by augmenting an MDP with a multi-action sample transition model which ...
| Authors | Ege C. Kaya, Mahsa Ghasemi, Abolfazl Hashemi |
|---|---|
| Primary category | cs.LG |
| Published | 6 March 2026 |
| Last updated at source | 16 August 2026 |
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