Amii researchers introduced Wayfarer, an architecture that allows reinforcement learning agents to discover their own skills without human input. The system combines three networks to build representations, create options, and solve tasks through a virtuous loop. Tested on the Atari 2600 challenging set, Wayfarer achieved a median performance boost of 2.5 times over learning without options. The approach outperformed established baselines like IQN and Rainbow while matching heavy model-based systems. This work aims to improve long-term decision-making in complex environments such as industrial robotics and autonomous vehicles.
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