5 citations · 7 across the 3 of their papers we have counts for
5 papers · 1 filter
ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs
Ted Moskovitz, Brendan O'Donoghue, Vivek Veeriah +3
In recent years, Reinforcement Learning (RL) has been applied to real-world problems with increasing success. Such applications often require to put constraints on the agent's beha…
Transfer RL via the Undo Maps Formalism
Abhi Gupta, Ted Moskovitz, David Alvarez-Melis +1
Transferring knowledge across domains is one of the most fundamental problems in machine learning, but doing so effectively in the context of reinforcement learning remains largely…
A First-Occupancy Representation for Reinforcement Learning
Ted Moskovitz, Spencer R. Wilson, Maneesh Sahani
Both animals and artificial agents benefit from state representations that support rapid transfer of learning across tasks and which enable them to efficiently traverse their envir…
Efficient Wasserstein Natural Gradients for Reinforcement Learning
Ted Moskovitz, Michael Arbel, Ferenc Huszar +1
A novel optimization approach is proposed for application to policy gradient methods and evolution strategies for reinforcement learning (RL). The procedure uses a computationally…
First-Order Preconditioning via Hypergradient Descent
Ted Moskovitz, Rui Wang, Janice Lan +4
Standard gradient descent methods are susceptible to a range of issues that can impede training, such as high correlations and different scaling in parameter space.These difficulti…