44 citations · 71 across the 11 of their papers we have counts for
11 papers
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 Unified Theory of Dual-Process Control
Ted Moskovitz, Kevin Miller, Maneesh Sahani +1
Dual-process theories play a central role in both psychology and neuroscience, figuring prominently in fields ranging from executive control to reward-based learning to judgment an…
Minimum Description Length Control
Ted Moskovitz, Ta-Chu Kao, Maneesh Sahani +1
We propose a novel framework for multitask reinforcement learning based on the minimum description length (MDL) principle. In this approach, which we term MDL-control (MDL-C), the…
Towards an Understanding of Default Policies in Multitask Policy Optimization
Ted Moskovitz, Michael Arbel, Jack Parker-Holder +1
Much of the recent success of deep reinforcement learning has been driven by regularized policy optimization (RPO) algorithms with strong performance across multiple domains. In th…
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…