A composite learning method for multi ship collision avoidance based on reinforcement learning and inverse control
Abstract Model free reinforcement learning methods have potentials in ship collision avoidance under unknown environments. To defect the low efficiency problem of the model free reinforcement learning, a composite learning method is proposed based on an asynchronous advantage actor critic (A3C) algorithm, a long short term memory neural network (LSTM) and Q learning. The proposed method uses Q learning for adaptive decisions between a LSTM inverse model based controller and the model free A3C policy. Multi ship collision avoidance simulations are conducted to verify the effectiveness of the model free A3C method, the proposed inverse model based method and the composite learning method. The simulation results indicate that the proposed composite learning based ship collision avoidance method outperforms the A3C learning method and a traditional optimization based method.
Neurocomputing