  This paper focuses on the impact of leveraging autonomous offensive
approaches in Deep Reinforcement Learning (DRL) to train more robust agents by
exploring the impact of applying adversarial learning to DRL for autonomous
security in Software Defined Networks (SDN). Two algorithms, Double Deep
Q-Networks (DDQN) and Neural Episodic Control to Deep Q-Network (NEC2DQN or
N2D), are compared. NEC2DQN was proposed in 2018 and is a new member of the
deep q-network (DQN) family of algorithms. The attacker has full observability
of the environment and access to a causative attack that uses state
manipulation in an attempt to poison the learning process. The implementation
of the attack is done under a white-box setting, in which the attacker has
access to the defender's model and experiences. Two games are played; in the
first game, DDQN is a defender and N2D is an attacker, and in second game, the
roles are reversed. The games are played twice; first, without an active
causative attack and secondly, with an active causative attack. For execution,
three sets of game results are recorded in which a single set consists of 10
game runs. The before and after results are then compared in order to see if
there was actually an improvement or degradation. The results show that with
minute parameter changes made to the algorithms, there was growth in the
attacker's role, since it is able to win games. Implementation of the
adversarial learning by the introduction of the causative attack showed the
algorithms are still able to defend the network according to their strengths.
