An Inverse Reinforcement Learning Method for Container Relocation in Container Terminal Yard During Loading

Abstract:

The container relocation during loading in the terminal yard has sequential and dynamic characteristics， and belongs to the non-deterministic polynomial hard problem. This paper takes the common container terminal yard， which is parallel to the shoreline， as the research object. Considering the relocation effect on the continuity and efficiency of shipment， the model based on Markov decision processes for the container relocation in the yard during loading was proposed， with the optimization objective to minimize the total relocation times， and the algorithm based on inverse reinforcement learning was designed. To verify the effectiveness of the algorithm， taking the random decision as criterion， the inverse reinforcement learning algorithm was compared with the common rule decision-making and the random decision-making . The results show that when the initial state of the bay is unsatisfactory， the common rule decision-making is not necessarily superior to random decision-making. The inverse reinforcement learning algorithm can effectively mine and apply the expert experience， and the probability of converging to the minimum relocation times is obviously better than that of the others. In addition， it can better control the relocation times of a single loading in different state of the bay， and realize the intelligent decision-making of container relocation during loading.