Reinforcement Learning Decision and Planning Algorithm Using Posterior Return Heuristic

doi:10.1109/TVT.2025.3614880

Reinforcement Learning Decision and Planning Algorithm Using Posterior Return Heuristic

2025-09-26

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DOI：10.1109/TVT.2025.3614880

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The complexities and dynamics of modern driving environments have amplified the uncertainties faced by autonomous driving systems, posing significant challenges to their decision-making processes. This paper introduces a novel reinforcement learning framework inspired by posterior return estimation, designed to enhance safety and foresight in robotic decision-making. To overcome the challenges of long-term dependency in reinforcement learning, a tree structure is utilized to record the interaction history between the agent and the environment. The posterior distribution of returns for each state is estimated from this historical data, with a neural network employed to model this distribution for enhanced decision-making. The framework's efficacy is validated through two decision-making and planning scenarios, where it surpasses several commonly used frameworks and demonstrates notably more anticipatory decision-making patterns.

Keywords:

Reinforcement learning

autonomous driving

decision making

posterior estimation

Reinforcement Learning Decision and Planning Algorithm Using Posterior Return Heuristic

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