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This work introduces a causal framework for planning under partial observability that treats distribution shifts as interventions, allowing agents to identify altered environmental components while preserving the tractability of $\alpha$-vector-based methods. By proving that the value function remains piecewise linear and convex in an augmented belief space, the approach maintains computational efficiency even when state distributions or dynamics change. This theoretical advance is significant for robust autonomous systems that must adapt strategies without retraining from scratch when facing real-world variability.
Planning under Distribution Shifts with Causal POMDPs
arXiv:2602.23545v2 Announce Type: replace Abstract: In the real world, planning is often challenged by distribution shifts. As such, a model of the environment obtained under one set of conditions may no longer remain valid as the distribution of states or the environment dynamics change, which in turn causes previously learned strategies to fail. In this work, we propose a theoretical framework for planning under partial observability using Partially Observable Markov Decision Processes (POMDPs) formulated using causal knowledge. By representing shifts in the environment as interventions on this causal POMDP, the framework enables evaluating plans under hypothesized changes and actively identifying which components of the environment have been altered. We show how to maintain and update a belief over both the latent state and the underlying domain, and we prove that the value function remains piecewise linear and convex (PWLC) in this augmented belief space. Preservation of PWLC under distribution shifts has the advantage of maintaining the tractability of planning via $\alpha$-vector-based POMDP methods.
Abstract:In the real world, planning is often challenged by distribution shifts. As such, a model of the environment obtained under one set of conditions may no longer remain valid as the distribution of states or the environment dynamics change, which in turn causes previously learned strategies to fail. In this work, we propose a theoretical framework for planning under partial observability using Partially Observable Markov Decision Processes (POMDPs) formulated using causal knowledge. By representing shifts in the environment as interventions on this causal POMDP, the framework enables evaluating plans under hypothesized changes and actively identifying which components of the environment have been altered. We show how to maintain and update a belief over both the latent state and the underlying domain, and we prove that the value function remains piecewise linear and convex (PWLC) in this augmented belief space. Preservation of PWLC under distribution shifts has the advantage of maintaining the tractability of planning via $\alpha$-vector-based POMDP methods.
Artificial Intelligence (cs.AI)
arXiv:2602.23545 [cs.AI]
(or arXiv:2602.23545v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2602.23545 Focus to learn more arXiv-issued DOI via DataCite
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