Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial Optimization

• URL: http://arxiv.org/abs/2306.01276v4
• Date: Wed, 17 Jul 2024 05:55:45 GMT
• Title: Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial Optimization
• Authors: Hyeonah Kim, Minsu Kim, Sungsoo Ahn, Jinkyoo Park,
• Abstract summary: We propose a simple but effective method, called symmetric replay training (SRT), which can be easily integrated into various Deep reinforcement learning (DRL) methods. Our method leverages high-reward samples to encourage exploration of symmetric regions without additional online interactions - free. Experimental results demonstrate the consistent improvement of our method in sample efficiency across diverse DRL methods applied to real-world tasks.
• Score: 42.92248233465095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep reinforcement learning (DRL) has significantly advanced the field of combinatorial optimization (CO). However, its practicality is hindered by the necessity for a large number of reward evaluations, especially in scenarios involving computationally intensive function assessments. To enhance the sample efficiency, we propose a simple but effective method, called symmetric replay training (SRT), which can be easily integrated into various DRL methods. Our method leverages high-reward samples to encourage exploration of the under-explored symmetric regions without additional online interactions - free. Through replay training, the policy is trained to maximize the likelihood of the symmetric trajectories of discovered high-rewarded samples. Experimental results demonstrate the consistent improvement of our method in sample efficiency across diverse DRL methods applied to real-world tasks, such as molecular optimization and hardware design.

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