94 citations · 449 across the 53 of their papers we have counts for
4 papers · 1 filter
Imitation-Projected Programmatic Reinforcement Learning
Abhinav Verma, Hoang M. Le, Yisong Yue +1
We study the problem of programmatic reinforcement learning, in which policies are represented as short programs in a symbolic language. Programmatic policies can be more interpret…
Control Regularization for Reduced Variance Reinforcement Learning
Richard Cheng, Abhinav Verma, Gabor Orosz +3
Dealing with high variance is a significant challenge in model-free reinforcement learning (RL). Existing methods are unreliable, exhibiting high variance in performance from run t…
Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustness
Greg Anderson, Shankara Pailoor, Isil Dillig +1
In recent years, the notion of local robustness (or robustness for short) has emerged as a desirable property of deep neural networks. Intuitively, robustness means that small pert…
Representing Formal Languages: A Comparison Between Finite Automata and Recurrent Neural Networks
Joshua J. Michalenko, Ameesh Shah, Abhinav Verma +3
We investigate the internal representations that a recurrent neural network (RNN) uses while learning to recognize a regular formal language. Specifically, we train a RNN on positi…