Fast Non-Episodic Finite-Horizon RL with K-Step Lookahead Thresholding

2026-06-16 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

The paper introduces a novel reinforcement learning approach, "K-step lookahead thresholding," for online learning in non-episodic, finite-horizon Markov Decision Processes (MDPs). This method addresses challenges in estimating returns to a fixed terminal time. It learns a K-step lookahead Q-function and selects actions only when their estimated K-step value exceeds a time-varying threshold. The proposed tabular learning algorithm achieves fast finite-sample convergence. It demonstrates minimax optimal constant regret for K=1 and ℱ(​max((K-1),C₊₁)√SATlog(T))​ regret for K≥ 2. Empirical evaluations across synthetic MDPs, JumpRiverswim, FrozenLake, and AnyTrading environments show superior cumulative rewards compared to six state-of-the-art tabular RL methods. An adaptive K variant (LG1-2T) balances initial convergence and long-term performance.

Key takeaway

For Machine Learning Engineers developing online reinforcement learning systems in finite-horizon, non-episodic environments, consider implementing K-step lookahead thresholding. This applies to areas like financial trading or medical regimens. This approach offers significantly faster convergence and higher cumulative rewards than traditional methods, especially when using an adaptive K-step strategy. You can warm-start existing RL algorithms with this technique to gain early performance benefits without sacrificing long-term optimality.

Key insights

K-step lookahead Q-functions with adaptive thresholding enable fast, sample-efficient online RL in finite-horizon MDPs.

Principles

• Limiting planning depth reduces value target complexity.
• Thresholding rapidly eliminates low-value actions.
• Adaptive lookahead balances convergence and reward.

Method

The algorithm learns a K-step lookahead Q-function, then selects actions if their estimated K-step value surpasses a time-varying threshold, adaptively increasing K over time.

In practice

• Apply K-step lookahead for non-episodic financial trading.
• Use adaptive K to warm-start standard RL algorithms.

Topics

• Reinforcement Learning
• Finite-Horizon MDPs
• K-Step Lookahead
• Thresholding Policies
• Sample Efficiency
• Tabular RL
• Online Learning

Code references

• AminHP/gym-anytrading

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.