21 citations · 37 across the 6 of their papers we have counts for
12 papers
Operator Splitting Value Iteration
Amin Rakhsha, Andrew Wang, Mohammad Ghavamzadeh +1
We introduce new planning and reinforcement learning algorithms for discounted MDPs that utilize an approximate model of the environment to accelerate the convergence of the value…
The act of remembering: a study in partially observable reinforcement learning
Rodrigo Toro Icarte, Richard Valenzano, Toryn Q. Klassen +3
Reinforcement Learning (RL) agents typically learn memoryless policies---policies that only consider the last observation when selecting actions. Learning memoryless policies is ef…
SOAR: Second-Order Adversarial Regularization
Avery Ma, Fartash Faghri, Nicolas Papernot +1
Adversarial training is a common approach to improving the robustness of deep neural networks against adversarial examples. In this work, we propose a novel regularization approach…
Policy-Aware Model Learning for Policy Gradient Methods
Romina Abachi, Mohammad Ghavamzadeh, Amir-massoud Farahmand
This paper considers the problem of learning a model in model-based reinforcement learning (MBRL). We examine how the planning module of an MBRL algorithm uses the model, and propo…
Frequency-based Search-control in Dyna
Yangchen Pan, Jincheng Mei, Amir-massoud Farahmand
Model-based reinforcement learning has been empirically demonstrated as a successful strategy to improve sample efficiency. In particular, Dyna is an elegant model-based architectu…
An implicit function learning approach for parametric modal regression
Yangchen Pan, Ehsan Imani, Martha White +1
For multi-valued functions---such as when the conditional distribution on targets given the inputs is multi-modal---standard regression approaches are not always desirable because…