Zero-shot Task Adaptation using Natural Language
- URL: http://arxiv.org/abs/2106.02972v1
- Date: Sat, 5 Jun 2021 21:39:04 GMT
- Title: Zero-shot Task Adaptation using Natural Language
- Authors: Prasoon Goyal, Raymond J. Mooney, Scott Niekum
- Abstract summary: We propose a novel setting where an agent is given both a demonstration and a description.
Our approach is able to complete more than 95% of target tasks when using template-based descriptions.
- Score: 43.807555235240365
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Imitation learning and instruction-following are two common approaches to
communicate a user's intent to a learning agent. However, as the complexity of
tasks grows, it could be beneficial to use both demonstrations and language to
communicate with an agent. In this work, we propose a novel setting where an
agent is given both a demonstration and a description, and must combine
information from both the modalities. Specifically, given a demonstration for a
task (the source task), and a natural language description of the differences
between the demonstrated task and a related but different task (the target
task), our goal is to train an agent to complete the target task in a zero-shot
setting, that is, without any demonstrations for the target task. To this end,
we introduce Language-Aided Reward and Value Adaptation (LARVA) which, given a
source demonstration and a linguistic description of how the target task
differs, learns to output a reward / value function that accurately describes
the target task. Our experiments show that on a diverse set of adaptations, our
approach is able to complete more than 95% of target tasks when using
template-based descriptions, and more than 70% when using free-form natural
language.
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