17 citations · 17 across the 3 of their papers we have counts for
7 papers
Multi-Objective Policy Gradients with Topological Constraints
Kyle Hollins Wray, Stas Tiomkin, Mykel J. Kochenderfer +1
Multi-objective optimization models that encode ordered sequential constraints provide a solution to model various challenging problems including encoding preferences, modeling a c…
GEM: Group Enhanced Model for Learning Dynamical Control Systems
Philippe Hansen-Estruch, Wenling Shang, Lerrel Pinto +2
Learning the dynamics of a physical system wherein an autonomous agent operates is an important task. Often these systems present apparent geometric structures. For instance, the t…
Dynamics Generalization via Information Bottleneck in Deep Reinforcement Learning
Xingyu Lu, Kimin Lee, Pieter Abbeel +1
Despite the significant progress of deep reinforcement learning (RL) in solving sequential decision making problems, RL agents often overfit to training environments and struggle t…
AvE: Assistance via Empowerment
Yuqing Du, Stas Tiomkin, Emre Kiciman +3
One difficulty in using artificial agents for human-assistive applications lies in the challenge of accurately assisting with a person's goal(s). Existing methods tend to rely on i…
Preventing Imitation Learning with Adversarial Policy Ensembles
Albert Zhan, Stas Tiomkin, Pieter Abbeel
Imitation learning can reproduce policies by observing experts, which poses a problem regarding policy privacy. Policies, such as human, or policies on deployed robots, can all be…
Predictive Coding for Boosting Deep Reinforcement Learning with Sparse Rewards
Xingyu Lu, Stas Tiomkin, Pieter Abbeel
While recent progress in deep reinforcement learning has enabled robots to learn complex behaviors, tasks with long horizons and sparse rewards remain an ongoing challenge. In this…