Search Results for author:
Found 6 papers, 2 papers with code
Towards Principled Representation Learning from Videos for Reinforcement Learning
We study two types of settings: one where there is iid noise in the observation, and a more challenging setting where there is also the presence of exogenous noise, which is non-iid noise that is temporally correlated, such as the motion of people or cars in the background.
Streaming Active Learning with Deep Neural Networks
Active learning is perhaps most naturally posed as an online learning problem.
Personalized Reward Learning with Interaction-Grounded Learning (IGL)
In an era of countless content offerings, recommender systems alleviate information overload by providing users with personalized content suggestions.
Interaction-Grounded Learning with Action-inclusive Feedback
Consider the problem setting of Interaction-Grounded Learning (IGL), in which a learner's goal is to optimally interact with the environment with no explicit reward to ground its policies.
A Ranking Game for Imitation Learning
We propose a new framework for imitation learning -- treating imitation as a two-player ranking-based game between a policy and a reward.
Efficiently Guiding Imitation Learning Agents with Human Gaze
Based on similarities between the attention of reinforcement learning agents and human gaze, we propose a novel approach for utilizing gaze data in a computationally efficient manner, as part of an auxiliary loss function, which guides a network to have higher activations in image regions where the human's gaze fixated.
