21 citations · 131 across the 37 of their papers we have counts for
6 papers · 2 filters
Representation Alignment in Neural Networks
Ehsan Imani, Wei Hu, Martha White
It is now a standard for neural network representations to be trained on large, publicly available datasets, and used for new problems. The reasons for why neural network represent…
Off-Policy Actor-Critic with Emphatic Weightings
Eric Graves, Ehsan Imani, Raksha Kumaraswamy +1
A variety of theoretically-sound policy gradient algorithms exist for the on-policy setting due to the policy gradient theorem, which provides a simplified form for the gradient. T…
Exploiting Action Impact Regularity and Exogenous State Variables for Offline Reinforcement Learning
Vincent Liu, James R. Wright, Martha White
Offline reinforcement learning -- learning a policy from a batch of data -- is known to be hard for general MDPs. These results motivate the need to look at specific classes of MDP…
Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences
Alan Chan, Hugo Silva, Sungsu Lim +3
Approximate Policy Iteration (API) algorithms alternate between (approximate) policy evaluation and (approximate) greedification. Many different approaches have been explored for a…
A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning
Andrew Patterson, Adam White, Martha White
Many reinforcement learning algorithms rely on value estimation, however, the most widely used algorithms -- namely temporal difference algorithms -- can diverge under both off-pol…
Scalable Online Recurrent Learning Using Columnar Neural Networks
Khurram Javed, Martha White, Rich Sutton
Structural credit assignment for recurrent learning is challenging. An algorithm called RTRL can compute gradients for recurrent networks online but is computationally intractable…