arXiv:2602.00781v2 Announce Type: replace Abstract: Online reinforcement learning in non-episodic, finite-horizon MDPs remains underexplored and is challenged by the need to estimate returns to a fixed terminal time. Existing infinite-horizon methods, which often rely on discounted contraction, do not naturally account for this fixed-horizon structure. We introduce a modified Q-function: rather than targeting the full-horizon, we learn a K-step lookahead Q-function that truncates planning to the next K steps. To further improve sample efficiency, we introduce a thresholding mechanism: actions
This development addresses a current challenge in online reinforcement learning for non-episodic, finite-horizon problems, offering a more efficient approach compared to existing methods that struggle with fixed terminal times.
Improved efficiency in finite-horizon RL could accelerate the development and deployment of AI systems in real-world applications where planning horizons are naturally limited.
The introduction of a K-step lookahead Q-function and thresholding mechanism provides a more sample-efficient way to train RL agents in specific problem settings, potentially lowering computational costs and training time.
- · AI developers
- · Robotics companies
- · Logistics and planning software providers
- · SaaS companies leveraging AI
- · Developers relying solely on traditional infinite-horizon RL methods
More robust and efficient AI agents in tasks requiring short to medium-term planning.
Faster iteration cycles for deploying RL solutions in industrial and commercial settings.
Displacement of human decision-making in complex operational planning roles as AI systems become more capable and cost-effective.
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Read at arXiv cs.LG