"Many recent works focus on using expensive reinforcement learning (RL) methods to solve this problem (Sermanet et al., 2018; Liu et al., 2017; Peng et al., 2018; Aytar et al., 2018). In contrast, high-fidelity imitation in humans is often cheap: in one-shot we can closely mimic a demonstration. Inspired by this, we introduce a meta-learning approach (MetaMimic — Figure 1) to learn high-fidelity one-shot imitation policies by off-policy RL. These policies, when deployed, require a single demonstration as input in order to mimic the new skill being demonstrated." ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T10119d5c27aad6be-Mc65e40a07ad2032c2be30416 Delivery options: https://agi.topicbox.com/groups/agi/subscription
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