42 citations · 53 across the 10 of their papers we have counts for
11 papers
Sequence Compression Speeds Up Credit Assignment in Reinforcement Learning
Aditya A. Ramesh, Kenny Young, Louis Kirsch +1
Temporal credit assignment in reinforcement learning is challenging due to delayed and stochastic outcomes. Monte Carlo targets can bridge long delays between action and consequenc…
The Benefits of Model-Based Generalization in Reinforcement Learning
Kenny Young, Aditya Ramesh, Louis Kirsch +1
Model-Based Reinforcement Learning (RL) is widely believed to have the potential to improve sample efficiency by allowing an agent to synthesize large amounts of imagined experienc…
Doubly-Asynchronous Value Iteration: Making Value Iteration Asynchronous in Actions
Tian Tian, Kenny Young, Richard S. Sutton
Value iteration (VI) is a foundational dynamic programming method, important for learning and planning in optimal control and reinforcement learning. VI proceeds in batches, where…
Hindsight Network Credit Assignment: Efficient Credit Assignment in Networks of Discrete Stochastic Units
Kenny Young
Training neural networks with discrete stochastic variables presents a unique challenge. Backpropagation is not directly applicable, nor are the reparameterization tricks used in n…
Hindsight Network Credit Assignment
Kenny Young
We present Hindsight Network Credit Assignment (HNCA), a novel learning method for stochastic neural networks, which works by assigning credit to each neuron's stochastic output ba…
Variance Reduced Advantage Estimation with Hindsight Credit Assignment
Kenny Young
Hindsight Credit Assignment (HCA) refers to a recently proposed family of methods for producing more efficient credit assignment in reinforcement learning. These methods work by ex…