5 citations · 9 across the 3 of their papers we have counts for
3 papers
cs.AI2020
Towards Sample Efficient Agents through Algorithmic Alignment
Mingxuan Li, Michael L. Littman
In this work, we propose and explore Deep Graph Value Network (DeepGV) as a promising method to work around sample complexity in deep reinforcement-learning agents using a message-…
cs.LG2020★ 5 cited
Deep Radial-Basis Value Functions for Continuous Control
Kavosh Asadi, Neev Parikh, Ronald E. Parr +2
A core operation in reinforcement learning (RL) is finding an action that is optimal with respect to a learned value function. This operation is often challenging when the learned…
cs.GT2012★ 4 cited
An Efficient Optimal-Equilibrium Algorithm for Two-player Game Trees
Michael L. Littman, Nishkam Ravi, Arjun Talwar +1
Two-player complete-information game trees are perhaps the simplest possible setting for studying general-sum games and the computational problem of finding equilibria. These games…