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Rapid online adaptation to changing tasks is an important problem in machinelearning and, recently, a focus of meta-reinforcement learning. However,reinforcement learning (RL) algorithms struggle in POMDP environments becausethe state of the system, essential in a RL framework, is not always visible.Additionally, hand-designed meta-RL architectures may not include suitablecomputational structures for specific learning problems. The evolution ofonline learning mechanisms, on the contrary, has the ability to incorporatelearning strategies into an agent that can (i) evolve memory when required and(ii) optimize adaptation speed to specific online learning problems. In thispaper, we exploit the highly adaptive nature of neuromodulated neural networksto evolve a controller that uses the latent space of an autoencoder in a POMDP.The analysis of the evolved networks reveals the ability of the proposedalgorithm to acquire inborn knowledge in a variety of aspects such as thedetection of cues that reveal implicit rewards, and the ability to evolvelocation neurons that help with navigation. The integration of inborn knowledgeand online plasticity enabled fast adaptation and better performance incomparison to some non-evolutionary meta-reinforcement learning algorithms. Thealgorithm proved also to succeed in the 3D gaming environment Malmo Minecraft.

Evolving Inborn Knowledge For Fast Adaptation in Dynamic POMDP Problems