16,982 research outputs found
Teaching Inverse Reinforcement Learners via Features and Demonstrations
Learning near-optimal behaviour from an expert's demonstrations typically
relies on the assumption that the learner knows the features that the true
reward function depends on. In this paper, we study the problem of learning
from demonstrations in the setting where this is not the case, i.e., where
there is a mismatch between the worldviews of the learner and the expert. We
introduce a natural quantity, the teaching risk, which measures the potential
suboptimality of policies that look optimal to the learner in this setting. We
show that bounds on the teaching risk guarantee that the learner is able to
find a near-optimal policy using standard algorithms based on inverse
reinforcement learning. Based on these findings, we suggest a teaching scheme
in which the expert can decrease the teaching risk by updating the learner's
worldview, and thus ultimately enable her to find a near-optimal policy.Comment: NeurIPS'2018 (extended version
Socially Aware Motion Planning with Deep Reinforcement Learning
For robotic vehicles to navigate safely and efficiently in pedestrian-rich
environments, it is important to model subtle human behaviors and navigation
rules (e.g., passing on the right). However, while instinctive to humans,
socially compliant navigation is still difficult to quantify due to the
stochasticity in people's behaviors. Existing works are mostly focused on using
feature-matching techniques to describe and imitate human paths, but often do
not generalize well since the feature values can vary from person to person,
and even run to run. This work notes that while it is challenging to directly
specify the details of what to do (precise mechanisms of human navigation), it
is straightforward to specify what not to do (violations of social norms).
Specifically, using deep reinforcement learning, this work develops a
time-efficient navigation policy that respects common social norms. The
proposed method is shown to enable fully autonomous navigation of a robotic
vehicle moving at human walking speed in an environment with many pedestrians.Comment: 8 page
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