Abstract: To solve the space rendezvous problem between spacecraft and non-cooperative targets and alleviate application limitations of deep reinforcement learning in continuous space, this paper proposes a pursuit-evasion game algorithm based on branching deep reinforcement learning to obtain the space rendezvous strategy. The differential game is used to solve the optimal control problem of space intersection for non-cooperative targets, which is described as a pursuit-evasion game problem under the action of continuous thrust. To avoid the dimension disaster of the traditional deep reinforcement learning in dealing with continuous space, this paper constructs a fuzzy inference model to represent the continuous space, and proposes a branching deep reinforcement learning architecture with multiple parallel neural networks and a shared decision module. The combination of optimal control and game theory is realized, effectively overcoming the difficulty in solving the highly nonlinear differential game model by the classical optimal control theory, and further improving the training ability of deep reinforcement learning on discrete behaviors. Finally, an example is given to verify the effectiveness of the algorithm.

Key words: non-cooperative targets, space rendezvous, pursuit-evasion problem of spacecraft, continuous space, differential game, deep reinforcement learning, branching architectures