812 citations · 1.4k across the 24 of their papers we have counts for
28 papers
Targeted Search Control in AlphaZero for Effective Policy Improvement
Alexandre Trudeau, Michael Bowling
AlphaZero is a self-play reinforcement learning algorithm that achieves superhuman play in chess, shogi, and Go via policy iteration. To be an effective policy improvement operator…
Interpolating Between Softmax Policy Gradient and Neural Replicator Dynamics with Capped Implicit Exploration
Dustin Morrill, Esra'a Saleh, Michael Bowling +1
Neural replicator dynamics (NeuRD) is an alternative to the foundational softmax policy gradient (SPG) algorithm motivated by online learning and evolutionary game theory. The NeuR…
Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games: Corrections
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…
Should Models Be Accurate?
Esra'a Saleh, John D. Martin, Anna Koop +2
Model-based Reinforcement Learning (MBRL) holds promise for data-efficiency by planning with model-generated experience in addition to learning with experience from the environment…
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…