arXiv:2605.28276v1 Announce Type: new Abstract: Reinforcement learning algorithms are commonly analyzed (and designed) under the Markov assumption. This is unrealistic, as most environments encountered in practice are either partially observable, or require function approximation that restricts the agent to access non-Markovian state features. We consider the problem of learning an optimal reactive policy in a finite environment with deterministic observations (or equivalently, hard state aggregation). We introduce a new algorithm, Committed Q-learning, and prove almost-sure convergence to the
This research addresses a long-standing challenge in reinforcement learning (RL) regarding non-Markovian environments, which is currently a significant hurdle for deploying advanced AI agents.
Improved reactive policy learning methods like Committed Q-learning could significantly enhance the reliability and performance of AI agents operating in complex, real-world scenarios.
The development proposes a new algorithm with proven convergence, potentially overcoming limitations of traditional Markovian assumptions in RL and expanding the applicability of agentic systems.
- · AI agents developers
- · Robotics industry
- · Automation sector
- · Developers relying solely on purely Markovian models
More robust and adaptable AI agents begin to be developed and deployed in partially observable environments.
Increased commercialization and widespread adoption of AI agents in various industries due to their enhanced reliability.
Accelerated collapse of white-collar workflows and rise of general-purpose autonomous systems as agentic software becomes more capable.
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