38 citations · 52 across the 2 of their papers we have counts for
7 papers
World Model as a Graph: Learning Latent Landmarks for Planning
Lunjun Zhang, Ge Yang, Bradly C. Stadie
Planning - the ability to analyze the structure of a problem in the large and decompose it into interrelated subproblems - is a hallmark of human intelligence. While deep reinforce…
Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning
Silviu Pitis, Harris Chan, Stephen Zhao +2
What goals should a multi-goal reinforcement learning agent pursue during training in long-horizon tasks? When the desired (test time) goal distribution is too distant to offer a u…
One-Shot Pruning of Recurrent Neural Networks by Jacobian Spectrum Evaluation
Matthew Shunshi Zhang, Bradly Stadie
Recent advances in the sparse neural network literature have made it possible to prune many large feed forward and convolutional networks with only a small quantity of data. Yet, t…
Transfer Learning for Estimating Causal Effects using Neural Networks
Sören R. Künzel, Bradly C. Stadie, Nikita Vemuri +3
We develop new algorithms for estimating heterogeneous treatment effects, combining recent developments in transfer learning for neural networks with insights from the causal infer…
Simulating the stochastic dynamics and cascade failure of power networks
Charles Matthews, Bradly Stadie, Jonathan Weare +2
For large-scale power networks, the failure of particular transmission lines can offload power to other lines and cause self-protection trips to activate, instigating a cascade of…
Some Considerations on Learning to Explore via Meta-Reinforcement Learning
Bradly C. Stadie, Ge Yang, Rein Houthooft +5
We consider the problem of exploration in meta reinforcement learning. Two new meta reinforcement learning algorithms are suggested: E-MAML and E-. Results are present…