  Reusing previously trained models is critical in deep reinforcement learning
to speed up training of new agents. However, it is unclear how to acquire new
skills when objectives and constraints are in conflict with previously learned
skills. Moreover, when retraining, there is an intrinsic conflict between
exploiting what has already been learned and exploring new skills. In soft
actor-critic (SAC) methods, a temperature parameter can be dynamically adjusted
to weight the action entropy and balance the explore $\times$ exploit
trade-off. However, controlling a single coefficient can be challenging within
the context of retraining, even more so when goals are contradictory. In this
work, inspired by neuroscience research, we propose a novel approach using
inhibitory networks to allow separate and adaptive state value evaluations, as
well as distinct automatic entropy tuning. Ultimately, our approach allows for
controlling inhibition to handle conflict between exploiting less risky,
acquired behaviors and exploring novel ones to overcome more challenging tasks.
We validate our method through experiments in OpenAI Gym environments.
