  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.
