Adaptive disassembly sequence planning for VR maintenance training via deep reinforcement learning

VR training equipped with meta-heuristic disassembly planning algorithms has been widely applied in pre-employment training in recent years. However, these algorithms are usually authored for specific sequences of a single product, and it remains a challenge to generalize them to maintenance training with unpredictable disassembly targets. As a promising method for settling dynamic and stochastic problems, deep reinforcement learning (DRL) provides a new insight to dynamically generate optimal sequences. This study introduces the deep Q-network (DQN), a successful DRL method, to fulfill adaptive disassembly sequence planning (DSP) for the VR maintenance training. Disassembly Petri net is established to describe the disassembly process, and then the DSP problem is defined as a Markov decision process that can be solved by DQN. Two neural networks are designed and updated asynchronously, and the training of DQN is further achieved by backpropagation of errors. Especially, we replace the long-term return in DQN with the fitness function of the genetic algorithm to avoid dependence on the immediate reward. Several experiments have been carried out to exhibit great potentials of our method in on-site maintenance where the fault is uncertain.