New Reinforcement Learning Framework for Partial Observability with Action-Triggered Observations
Researchers have introduced Action-Triggered Sporadically Traceable Markov Decision Processes (ATST-MDPs), a reinforcement learning framework where full state observations occur randomly with probabilities determined by the agent's chosen actions. The work derives tailored Bellman equations, proves the existence of an optimal policy, and under a linear MDP assumption produces an algorithm called ATST-LSVI-UCB with provable regret bounds. This matters because it extends theoretical guarantees from fully observable linear MDPs to a realistic partial-observability setting without sacrificing the known optimal regret rate.
The paper presents ATST-MDPs, a new formal framework for reinforcement learning in which an agent does not always observe the full environment state; instead, full observations arrive stochastically at each timestep, with the probability governed by whichever action the agent selects. The authors derive Bellman equations specific to this setting and prove that an optimal policy exists. A key structural insight is that sporadic full observations allow the problem to be reformulated so that agents commit to entire action-sequences between consecutive observations, simplifying analysis. Under the linear MDP assumption, the value function over these action-sequences admits a finite-dimensional linear representation, making standard regression-based learning methods applicable. Building on this, the authors develop ATST-LSVI-UCB, an optimistic algorithm for episodic learning with geometrically distributed episode lengths. The algorithm achieves a regret bound of Õ(√(K d³ (1−γ)⁻³)), where K is the number of episodes, d the feature dimension, and γ the discount factor, matching the best-known rate for fully observable linear MDPs. The work thus demonstrates that action-dependent partial observability need not incur additional regret costs relative to the fully observable baseline.
What's missing
The paper is a preprint and has not yet undergone formal peer review. Key open questions include whether the regret bound is tight (i.e., whether a matching lower bound exists for the ATST-MDP setting), how the framework performs empirically on benchmark tasks, and whether the linear MDP assumption can be relaxed to cover more general function approximation. The practical impact of the action-dependent observation probability on exploration strategies beyond the UCB approach is also not addressed.
What different sources said
- arXiv cs.LGCenter
Reinforcement Learning with Action-Triggered Observations
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