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

General policy mapping: online continual reinforcement learning inspired on the insect brain