2020
AAAI
AAAI 2020
Combating False Negatives in Adversarial Imitation Learning (Student Abstract)
Abstract
Abstract We define the False Negatives problem and show that it is a significant limitation in adversarial imitation learning. We propose a method that solves the problem by leveraging the nature of goal-conditioned tasks. The method, dubbed Fake Conditioning, is tested on instruction following tasks in BabyAI environments, where it improves sample efficiency over the baselines by at least an order of magnitude.
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Interdisciplinary Bridge
- Artificial Intelligence and Reinforcement Learning
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Hot Topic Early Bird
- instruction following
Authors
Topics
Artificial Intelligence > Core AI > Agent Systems
Machine Learning > Learning Types > Adversarial Learning
Machine Learning > Learning Types > Weakly Supervised Learning
Reinforcement Learning > Methods > Deep RL
Machine Learning > Learning Types > Reinforcement Learning
Machine Learning > Learning Types > Imitation Learning