3 accepted papers
Reward design in reinforcement learning (RL) is challenging since specifying human notions of desired behavior may be difficult via reward functions or require many expert demonstrations. Can we instead cheaply design rewards using a natural language interface? This paper explores how to simplify re…
The state features available to a robot define the variables on which the learning computation depends. However, little prior work considers feature selection in the context of deploying a general-purpose robot able to learn new tasks. In this work, we explore human-driven feature selection in which…
Active learning literature has explored the selection of optimal queries by a learning agent with respect to given criteria, but prior work in classification has focused only on obtaining labels for queried samples. In contrast, proficient learners, like humans, integrate multiple forms of informati…