機械学習国際会議 ICML 2009

概要 †

• 機械学習国際会議（International Conference on Machine Learning）
• 2009年6月14日-18日
• モントリオール
• http://www.cs.mcgill.ca/~icml2009/

強化学習に最も関係が強い国際会議です．

今年は強化学習に関するセッションが3つあります． （昨年は6つもありました．）

1C - Exploration in Reinforcement Learning †

• The Adaptive k-Meteorologists Problem and Its Application to Structure Learning and Feature Selection in Reinforcement Learning.
  Carlos Diuk, Lihong Li and Bethany Leffler.
• Near-Bayesian Exploration in Polynomial Time.
  J. Zico Kolter and Andrew Ng.
• Optimistic Initialization and Greediness Lead to Polynomial Time Learning in Factored MDPs.
  Istvan Szita and Andras Lorincz.
• Dynamic Analysis of Multiagent Q-learning with e-greedy Exploration.
  Eduardo Rodrigues Gomes and Ryszard Kowalczyk.
• Hoeffding and Bernstein Races for Selecting Policies in Evolutionary Direct Policy Search.
  Verena Heidrich-Meisner and Christian Igel.

3C - Reinforcement Learning with Temporal Differences †

• Proto-Predictive Representation of States with Simple Recurrent Temporal-Difference Networks.
  Takaki Makino.
• Regularization and Feature Selection in Least Squares Temporal-Difference Learning.
  J. Zico Kolter and Andrew Ng.
• Fast gradient-descent methods for temporal-difference learning with linear function approximation.
  Richard S. Sutton, Hamid Reza Maei, Doina Precup, Shalabh Bhatnagar, David Silver, Csaba Szepesvari, Eric Wiewiora.
• Kernelized Value Function Approximation for Reinforcement Learning.
  Gavin Taylor and Ronald Parr.
• Constraint Relaxation in Approximate Linear Programs.
  Marek Petrik and Shlomo Zilberstein.

5C - Reinforcement Learning in High Order Environments †

• Binary Action Search for Learning Continuous-Action Control Policies.
  Jason Pazis and Michail Lagoudakis.
• Predictive Representations for Policy Gradient in POMDPs.
  Stochastic Search using the Natural Gradient.
• Stochastic Search using the Natural Gradient.
  Sun Yi, Daan Wierstra, Tom Schaul, and Juergen Schmidhuber.
• Approximate Inference for Planning in Stochastic Relational Worlds.
  Tobias Lang and Marc Toussaint.
• Discovering Options from Example Trajectories.
  Peng Zang, Peng Zhou, David Minnen and Charles Isbell.