Information Bottleneck-Enhanced Reinforcement Learning for Solving Operation Research Problems.

Area of Science:

• Artificial Intelligence
• Operations Research
• Machine Learning

Background:

• Reinforcement learning (RL) faces challenges in high-dimensional state spaces and unstable training for combinatorial optimization problems.
• Applications in operations research (OR) and smart manufacturing require robust decision-making frameworks.

Purpose of the Study:

• To introduce Information Bottleneck-Enhanced Reinforcement Learning (IBE), a novel framework designed to improve RL performance in structured combinatorial optimization.
• To enhance representation learning and exploration efficiency in RL for complex industrial decision-making.

Main Methods:

• IBE integrates information-theoretic regularization into attention-based RL architectures.
• It employs two bottleneck objectives: a state representation bottleneck for compact data representation and a policy bottleneck for exploration bonus.
• The framework utilizes mutual information between states and actions for policy regularization.

Main Results:

• IBE demonstrated superior performance and stability compared to established RL baselines (PPO, REINFORCE, AM, NeuOpt).
• Evaluations on routing and scheduling problems in logistics and manufacturing showed consistent outperformance.
• Ablation studies validated the synergistic effect of the two bottleneck components.

Conclusions:

• IBE offers a principled and generalizable approach to enhance RL for combinatorial optimization and Industry 4.0 environments.
• The framework effectively addresses challenges in representation learning and exploration for complex decision spaces.
• IBE provides a robust solution for real-world industrial decision-making applications.