Reinforcement learning driven heuristic optimization combines the strengths of reinforcement learning and heuristic optimization to solve challenging decision-making problems. It utilizes reinforcement learning’s ability to learn optimal policies from experience and heuristic optimization’s domain-specific knowledge to guide the exploration process. This approach dynamically adjusts the heuristics based on the feedback received from the reinforcement learning algorithm, leading to improved performance over time.
