  In this paper, an adaptive cooperative guidance strategy for the active
protection of a target spacecraft trying to evade an interceptor was developed.
The target spacecraft performs evasive maneuvers, launching an active defense
vehicle to divert the interceptor. Instead of classical strategies, which are
based on optimal control or differential game theory, the problem was solved by
using the deep reinforcement learning method, and imperfect information was
assumed for the interceptor maneuverability. To address the sparse reward
problem, a universal reward design method and an increasingly difficult
training approach were presented utilizing the shaping technique. Guidance law,
reward function, and training approach were demonstrated through the learning
process and Monte Carlo simulations. The application of the non-sparse reward
function and increasingly difficult training approach accelerated the model
convergence, alleviating the overfitting problem. Considering a standard
optimal guidance law as a benchmark, the effectiveness, and the advantages,
that guarantee the target spacecraft's escape and win rates in a multi-agent
game, of the proposed guidance strategy were validated by the simulation
results. The trained agent's adaptiveness to the interceptor maneuverability
was superior to the optimal guidance law. Moreover, compared to the standard
optimal guidance law, the proposed guidance strategy performed better with less
prior knowledge.
