Reinforcement learning for dialog management using least-squares Policy iteration and fast feature selection

2009 ◽  
Author(s):  
Lihong Li ◽  
Jason D. Williams ◽  
Suhrid Balakrishnan
Keyword(s):  
Feature Selection ◽  
Reinforcement Learning ◽  
Least Squares ◽  
Policy Iteration ◽  
Dialog Management

Discriminative Least Squares Regression for Multiclass Classification and Feature Selection

2012 ◽  
Vol 23 (11) ◽  
pp. 1738-1754 ◽  
Author(s):  
Shiming Xiang ◽  
Feiping Nie ◽  
Gaofeng Meng ◽  
Chunhong Pan ◽  
Changshui Zhang
Keyword(s):  
Feature Selection ◽  
Least Squares ◽  
Multiclass Classification ◽  
Least Squares Regression

Multi-agent reinforcement learning using ordinal action selection and approximate policy iteration

2016 ◽  
Vol 14 (06) ◽  
pp. 1650053
Author(s):  
Daxue Liu ◽  
Jun Wu ◽  
Xin Xu
Keyword(s):  
Reinforcement Learning ◽  
Single Agent ◽  
Action Selection ◽  
Policy Iteration ◽  
Common Interest ◽  
Policy Space ◽  
Markov Games ◽  
Approximate Policy Iteration ◽  
Multi Agent ◽  
Agent Coordination

Multi-agent reinforcement learning (MARL) provides a useful and flexible framework for multi-agent coordination in uncertain dynamic environments. However, the generalization ability and scalability of algorithms to large problem sizes, already problematic in single-agent RL, is an even more formidable obstacle in MARL applications. In this paper, a new MARL method based on ordinal action selection and approximate policy iteration called OAPI (Ordinal Approximate Policy Iteration), is presented to address the scalability issue of MARL algorithms in common-interest Markov Games. In OAPI, an ordinal action selection and learning strategy is integrated with distributed approximate policy iteration not only to simplify the policy space and eliminate the conflicts in multi-agent coordination, but also to realize the approximation of near-optimal policies for Markov Games with large state spaces. Based on the simplified policy space using ordinal action selection, the OAPI algorithm implements distributed approximate policy iteration utilizing online least-squares policy iteration (LSPI). This resulted in multi-agent coordination with good convergence properties with reduced computational complexity. The simulation results of a coordinated multi-robot navigation task illustrate the feasibility and effectiveness of the proposed approach.

scholarly journals Least-Squares Methods for Policy Iteration

2012 ◽  
pp. 75-109 ◽  
Author(s):  
Lucian Buşoniu ◽  
Alessandro Lazaric ◽  
Mohammad Ghavamzadeh ◽  
Rémi Munos ◽  
Robert Babuška ◽  
...  
Keyword(s):  
Least Squares ◽  
Policy Iteration
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