33 research outputs found
Active Inverse Reward Design
Designers of AI agents often iterate on the reward function in a
trial-and-error process until they get the desired behavior, but this only
guarantees good behavior in the training environment. We propose structuring
this process as a series of queries asking the user to compare between
different reward functions. Thus we can actively select queries for maximum
informativeness about the true reward. In contrast to approaches asking the
designer for optimal behavior, this allows us to gather additional information
by eliciting preferences between suboptimal behaviors. After each query, we
need to update the posterior over the true reward function from observing the
proxy reward function chosen by the designer. The recently proposed Inverse
Reward Design (IRD) enables this. Our approach substantially outperforms IRD in
test environments. In particular, it can query the designer about
interpretable, linear reward functions and still infer non-linear ones
Interactive Teaching Algorithms for Inverse Reinforcement Learning
We study the problem of inverse reinforcement learning (IRL) with the added
twist that the learner is assisted by a helpful teacher. More formally, we
tackle the following algorithmic question: How could a teacher provide an
informative sequence of demonstrations to an IRL learner to speed up the
learning process? We present an interactive teaching framework where a teacher
adaptively chooses the next demonstration based on learner's current policy. In
particular, we design teaching algorithms for two concrete settings: an
omniscient setting where a teacher has full knowledge about the learner's
dynamics and a blackbox setting where the teacher has minimal knowledge. Then,
we study a sequential variant of the popular MCE-IRL learner and prove
convergence guarantees of our teaching algorithm in the omniscient setting.
Extensive experiments with a car driving simulator environment show that the
learning progress can be speeded up drastically as compared to an uninformative
teacher.Comment: IJCAI'19 paper (extended version