  Despite definite success in deep reinforcement learning problems,
actor-critic algorithms are still confronted with sample inefficiency in
complex environments, particularly in tasks where efficient exploration is a
bottleneck. These methods consider a policy (the actor) and a value function
(the critic) whose respective losses are built using different motivations and
approaches. This paper introduces a third protagonist: the adversary. While the
adversary mimics the actor by minimizing the KL-divergence between their
respective action distributions, the actor, in addition to learning to solve
the task, tries to differentiate itself from the adversary predictions. This
novel objective stimulates the actor to follow strategies that could not have
been correctly predicted from previous trajectories, making its behavior
innovative in tasks where the reward is extremely rare. Our experimental
analysis shows that the resulting Adversarially Guided Actor-Critic (AGAC)
algorithm leads to more exhaustive exploration. Notably, AGAC outperforms
current state-of-the-art methods on a set of various hard-exploration and
procedurally-generated tasks.
