79 citations · 91 across the 7 of their papers we have counts for
7 papers
Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners
Haseeb Shah, Lingwei Zhu, Adam White +1
Reinforcement learning is increasingly being considered for controlling real-world systems, from fusion plasma and autonomous vehicles to drug discovery and drinking water treatmen…
Symmetric Behavior Regularized Policy Optimization
Lingwei Zhu, Haseeb Shah, Zheng Chen +1
Behavior Regularized Policy Optimization (BRPO) leverages asymmetric divergence regularization to mitigate distribution shift in offline reinforcement learning. This paper is the f…
q-exponential family for policy optimization
Lingwei Zhu, Haseeb Shah, Han Wang +2
Policy optimization methods benefit from a simple and tractable policy parametrization, usually the Gaussian for continuous action spaces. In this paper, we consider a broader poli…
GVFs in the Real World: Making Predictions Online for Water Treatment
Muhammad Kamran Janjua, Haseeb Shah, Martha White +3
In this paper we investigate the use of reinforcement-learning based prediction approaches for a real drinking-water treatment plant. Developing such a prediction system is a criti…
Scalable Real-Time Recurrent Learning Using Columnar-Constructive Networks
Khurram Javed, Haseeb Shah, Rich Sutton +1
Constructing states from sequences of observations is an important component of reinforcement learning agents. One solution for state construction is to use recurrent neural networ…
An Open-World Extension to Knowledge Graph Completion Models
Haseeb Shah, Johannes Villmow, Adrian Ulges +2
We present a novel extension to embedding-based knowledge graph completion models which enables them to perform open-world link prediction, i.e. to predict facts for entities unsee…
Distillation Techniques for Pseudo-rehearsal Based Incremental Learning
Haseeb Shah, Khurram Javed, Faisal Shafait
The ability to learn from incrementally arriving data is essential for any life-long learning system. However, standard deep neural networks forget the knowledge about the old task…