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Exploration and reward specification are fundamental and intertwinedchallenges for reinforcement learning. Solving sequential decision-making tasksrequiring expansive exploration requires either careful design of rewardfunctions or the use of novelty-seeking exploration bonuses. Human supervisorscan provide effective guidance in the loop to direct the exploration process,but prior methods to leverage this guidance require constant synchronoushigh-quality human feedback, which is expensive and impractical to obtain. Inthis work, we present a technique called Human Guided Exploration (HuGE), whichuses low-quality feedback from non-expert users that may be sporadic,asynchronous, and noisy. HuGE guides exploration for reinforcement learning notonly in simulation but also in the real world, all without meticulous rewardspecification. The key concept involves bifurcating human feedback and policylearning: human feedback steers exploration, while self-supervised learningfrom the exploration data yields unbiased policies. This procedure can leveragenoisy, asynchronous human feedback to learn policies with no hand-craftedreward design or exploration bonuses. HuGE is able to learn a variety ofchallenging multi-stage robotic navigation and manipulation tasks in simulationusing crowdsourced feedback from non-expert users. Moreover, this paradigm canbe scaled to learning directly on real-world robots, using occasional,asynchronous feedback from human supervisors.

Breadcrumbs to the Goal: Goal-Conditioned Exploration from Human-in-the-Loop Feedback