14 citations · 25 across the 5 of their papers we have counts for
5 papers
What to Do When Your Discrete Optimization Is the Size of a Neural Network?
Hugo Silva, Martha White
Oftentimes, machine learning applications using neural networks involve solving discrete optimization problems, such as in pruning, parameter-isolation-based continual learning and…
Coagent Networks: Generalized and Scaled
James E. Kostas, Scott M. Jordan, Yash Chandak +5
Coagent networks for reinforcement learning (RL) [Thomas and Barto, 2011] provide a powerful and flexible framework for deriving principled learning rules for arbitrary stochastic…
The In-Sample Softmax for Offline Reinforcement Learning
Chenjun Xiao, Han Wang, Yangchen Pan +2
Reinforcement learning (RL) agents can leverage batches of previously collected data to extract a reasonable control policy. An emerging issue in this offline RL setting, however,…
Accelerated Gradient Temporal Difference Learning
Yangchen Pan, Adam White, Martha White
The family of temporal difference (TD) methods span a spectrum from computationally frugal linear methods like TD(λ) to data efficient least squares methods. Least square methods m…
A Greedy Approach to Adapting the Trace Parameter for Temporal Difference Learning
Martha White, Adam White
One of the main obstacles to broad application of reinforcement learning methods is the parameter sensitivity of our core learning algorithms. In many large-scale applications, onl…