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You Only Live Once: Single-Life Reinforcement Learning via Learned Reward Shaping
Annie Chen · Archit Sharma · Sergey Levine · Chelsea Finn

Reinforcement learning algorithms are typically designed to learn a performant policy that can repeatedly and autonomously complete a task, typically starting from scratch. However, many real-world situations operate under a different set of assumptions: the goal might not be to learn a policy that can do the task repeatedly, but simply to perform a new task successfully once, ideally as quickly as possible, and while leveraging some prior knowledge or experience. For example, imagine a robot that is exploring another planet, where it cannot get help or supervision from humans. If it needs to navigate to a crater that it has never seen before in search of water, it does not really need to acquire a policy for reaching craters reliably, it only needs to reach this particular crater once. It must do so without the benefit of episodic resets and tackle a new, unknown terrain, but it can leverage prior experience it acquired on Earth. We formalize this problem setting, which we call single-life reinforcement learning (SLRL), where an agent must complete a task once while contending with some form of novelty in a single trial without interventions, given some prior data. In this setting, we find that algorithms designed for standard episodic reinforcement learning can struggle, as they have trouble recovering from novel states especially when informative rewards are not provided. Motivated by this observation, we also propose an algorithm, $Q$-weighted adversarial learning (QWALE), that addresses the dearth of supervision by employing a distribution matching strategy that leverages the agent's prior experience as guidance in novel situations. Our experiments on several single-life continuous control problems indicate that methods based on our distribution matching formulation are 20-60% more successful because they can more quickly recover from novel, out-of-distribution states.

#### Author Information

##### Chelsea Finn (Stanford)

Chelsea Finn is an Assistant Professor in Computer Science and Electrical Engineering at Stanford University. Finn's research interests lie in the capability of robots and other agents to develop broadly intelligent behavior through learning and interaction. To this end, her work has included deep learning algorithms for concurrently learning visual perception and control in robotic manipulation skills, inverse reinforcement methods for learning reward functions underlying behavior, and meta-learning algorithms that can enable fast, few-shot adaptation in both visual perception and deep reinforcement learning. Finn received her Bachelor's degree in Electrical Engineering and Computer Science at MIT and her PhD in Computer Science at UC Berkeley. Her research has been recognized through the ACM doctoral dissertation award, the Microsoft Research Faculty Fellowship, the C.V. Ramamoorthy Distinguished Research Award, and the MIT Technology Review 35 under 35 Award, and her work has been covered by various media outlets, including the New York Times, Wired, and Bloomberg. Throughout her career, she has sought to increase the representation of underrepresented minorities within CS and AI by developing an AI outreach camp at Berkeley for underprivileged high school students, a mentoring program for underrepresented undergraduates across four universities, and leading efforts within the WiML and Berkeley WiCSE communities of women researchers.