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Unsupervised Curricula for Visual Meta-Reinforcement Learning
Allan Jabri · Kyle Hsu · Abhishek Gupta · Benjamin Eysenbach · Sergey Levine · Chelsea Finn

Wed Dec 11 10:30 AM -- 10:35 AM (PST) @ West Exhibition Hall A

Meta-reinforcement learning algorithms leverage experience across many tasks to learn fast and effective reinforcement learning (RL) algorithms. However, current meta-RL methods depend critically on a manually-defined distribution of meta-training tasks, and hand-crafting these task distributions is challenging and time-consuming. We develop an unsupervised algorithm for inducing an adaptive meta-training task distribution, i.e. an automatic curriculum, by modeling unsupervised interaction in a visual environment. Crucially, the task distribution is scaffolded by the meta-learner's behavior, with density-based exploration driving the evolution of the task distribution. We formulate unsupervised meta-RL with an information-theoretic objective optimized via expectation-maximization over trajectory-level latent variables. Repeating this procedure leads to iterative reorganization of behavior, allowing the task distribution to adapt as the meta-learner becomes more competent. In our experiments on vision-based navigation and manipulation domains, we show that our algorithm allows for unsupervised meta-learning of skills that transfer to downstream tasks specified by human-provided reward functions, as well as pre-training for more efficient meta-learning on user-defined task distributions. To understand the nature of the curricula, we provide visualizations and analysis of the task distributions discovered throughout the learning process, finding that the emergent tasks span a range of environment-specific exploratory and exploitative behavior.

Author Information

Allan Jabri (UC Berkeley)
Kyle Hsu (University of Toronto)
Abhishek Gupta (University of California, Berkeley)
Benjamin Eysenbach (Carnegie Mellon University)
Sergey Levine (UC Berkeley)
Chelsea Finn (Stanford University)

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