Dynamic programming is a potent optimization technique that involves decomposing a complex problem into smaller sub-problems and recursively solving them. Traditional dynamic programming systems, on the other hand, are frequently unable to adapt to changing conditions or manage ambiguous or incomplete data. In contrast, reinforcement learning is a technique for machine learning that incorporates feedback-based learning to enhance decision-making. This paper proposes a novel framework called DynaRLF, which is an algorithmic paradigm that combines dynamic programming and reinforcement learning to more effectively and efficiently address complex optimization problems in domains of artificial intelligence, robotics automation and designing hybrid control mechanisms. DynaRLF’s primary feature is its utilization of reinforcement learning to enhance the adaptability and efficiency of existing dynamic programming methodologies.