812 citations · 1.4k across the 24 of their papers we have counts for
4 papers · 1 filter
The Partially Observable History Process
Dustin Morrill, Amy R. Greenwald, Michael Bowling
We introduce the partially observable history process (POHP) formalism for reinforcement learning. POHP centers around the actions and observations of a single agent and abstracts…
Learning to Be Cautious
Montaser Mohammedalamen, Dustin Morrill, Alexander Sieusahai +2
A key challenge in the field of reinforcement learning is to develop agents that behave cautiously in novel situations. It is generally impossible to anticipate all situations that…
Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games
Dustin Morrill, Ryan D'Orazio, Marc Lanctot +3
Hindsight rationality is an approach to playing general-sum games that prescribes no-regret learning dynamics for individual agents with respect to a set of deviations, and further…
Solving Common-Payoff Games with Approximate Policy Iteration
Samuel Sokota, Edward Lockhart, Finbarr Timbers +6
For artificially intelligent learning systems to have widespread applicability in real-world settings, it is important that they be able to operate decentrally. Unfortunately, dece…