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IJCAI
2007
13 years 8 months ago
Utile Distinctions for Relational Reinforcement Learning
We introduce an approach to autonomously creating state space abstractions for an online reinforcement learning agent using a relational representation. Our approach uses a tree-b...
William Dabney, Amy McGovern
AIPS
2010
13 years 9 months ago
When Policies Can Be Trusted: Analyzing a Criteria to Identify Optimal Policies in MDPs with Unknown Model Parameters
Computing a good policy in stochastic uncertain environments with unknown dynamics and reward model parameters is a challenging task. In a number of domains, ranging from space ro...
Emma Brunskill
CGF
2010
141views more  CGF 2010»
13 years 4 months ago
A Survey of Procedural Noise Functions
Procedural noise functions are widely used in Computer Graphics, from off-line rendering in movie production to interactive video games. The ability to add complex and intricate d...
Ares Lagae, Sylvain Lefebvre, R. Cook, T. DeRose, ...
STOC
2012
ACM
251views Algorithms» more  STOC 2012»
11 years 9 months ago
Minimax option pricing meets black-scholes in the limit
Option contracts are a type of financial derivative that allow investors to hedge risk and speculate on the variation of an asset’s future market price. In short, an option has...
Jacob Abernethy, Rafael M. Frongillo, Andre Wibiso...
ATAL
2008
Springer
13 years 8 months ago
On the usefulness of opponent modeling: the Kuhn Poker case study
The application of reinforcement learning algorithms to Partially Observable Stochastic Games (POSG) is challenging since each agent does not have access to the whole state inform...
Alessandro Lazaric, Mario Quaresimale, Marcello Re...