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ICLR 2019 Task-Agnostic Reinforcement Learning Workshop Task-Agnostic Reinforcement Learning Workshop at ICLR, 06 May 2019, New Orleans Building agents that explore and learn in the absence of rewards Speakers Dates Schedule Papers Organizers Summary Many of the successes in deep learning build upon rich supervision. Reinforcement learning (RL) is no exception to this: algorithms for locomotion, manipulation, and game playing often rely on carefully crafted reward functions that guide the agent. But defining dense rewards becomes impractical for complex tasks. Moreover, attempts to do so frequently result in agents exploiting human error in the specification. To scale RL to the next level of difficulty, agents will have to learn autonomously in the absence of rewards. We define task-agnostic reinforcement learning (TARL) as learning in an environment without rewards to later quickly solve down-steam tasks. Active research questions in TARL include designing object...
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