  Green Security Games (GSGs) have been proposed and applied to optimize
patrols conducted by law enforcement agencies in green security domains such as
combating poaching, illegal logging and overfishing. However, real-time
information such as footprints and agents' subsequent actions upon receiving
the information, e.g., rangers following the footprints to chase the poacher,
have been neglected in previous work. To fill the gap, we first propose a new
game model GSG-I which augments GSGs with sequential movement and the vital
element of real-time information. Second, we design a novel deep reinforcement
learning-based algorithm, DeDOL, to compute a patrolling strategy that adapts
to the real-time information against a best-responding attacker. DeDOL is built
upon the double oracle framework and the policy-space response oracle, solving
a restricted game and iteratively adding best response strategies to it through
training deep Q-networks. Exploring the game structure, DeDOL uses
domain-specific heuristic strategies as initial strategies and constructs
several local modes for efficient and parallelized training. To our knowledge,
this is the first attempt to use Deep Q-Learning for security games.
