arXiv:2510.02149v2 Announce Type: replace Abstract: We introduce Action-Triggered Sporadically Traceable Markov Decision Processes (ATST-MDPs), a reinforcement learning framework for partial observability in which full state observations occur stochastically at each step, with probability determined by the chosen action. We derive Bellman equations tailored to this setting and establish the existence of an optimal policy. Exploiting the fact that sporadic observations reveal the full state, we provide an equivalent formulation in which agents commit to action-sequences between consecutive obse
This research is emerging now as advanced reinforcement learning methods are increasingly applied to complex, partially observable real-world problems requiring more robust theoretical foundations.
A strategic reader should care because improving RL's ability to operate effectively with sporadic, action-triggered observations is crucial for autonomous systems in environments where continuous full state access is impractical or impossible.
The development of ATST-MDPs provides a new theoretical framework and potential algorithms for building more resilient and efficient AI agents that can learn and act under realistic partial observability constraints.
- · AI agents
- · Reinforcement learning researchers
- · Autonomous systems developers
- · Robotics
- · Traditional RL approaches with full state assumptions
Improved performance and reliability of AI agents operating in complex, real-world environments with intermittent data.
Acceleration of autonomous system deployment in fields like logistics, exploration, and industrial control where full state visibility is rare.
Enhanced AI capabilities contributing to the broader development of general-purpose AI and more sophisticated agentic systems.
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Read at arXiv cs.LG