  We have developed a model for online continual or lifelong reinforcement
learning (RL) inspired on the insect brain. Our model leverages the offline
training of a feature extraction and a common general policy layer to enable
the convergence of RL algorithms in online settings. Sharing a common policy
layer across tasks leads to positive backward transfer, where the agent
continuously improved in older tasks sharing the same underlying general
policy. Biologically inspired restrictions to the agent's network are key for
the convergence of RL algorithms. This provides a pathway towards efficient
online RL in resource-constrained scenarios.
