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ICRA
2009
IEEE
143views Robotics» more  ICRA 2009»
14 years 2 months ago
Least absolute policy iteration for robust value function approximation
Abstract— Least-squares policy iteration is a useful reinforcement learning method in robotics due to its computational efficiency. However, it tends to be sensitive to outliers...
Masashi Sugiyama, Hirotaka Hachiya, Hisashi Kashim...
ECML
2004
Springer
14 years 1 months ago
Dynamic Asset Allocation Exploiting Predictors in Reinforcement Learning Framework
Given the pattern-based multi-predictors of the stock price, we study a method of dynamic asset allocation to maximize the trading performance. To optimize the proportion of asset ...
Jangmin O, Jae Won Lee, Jongwoo Lee, Byoung-Tak Zh...
ECML
2004
Springer
14 years 1 months ago
Batch Reinforcement Learning with State Importance
Abstract. We investigate the problem of using function approximation in reinforcement learning where the agent’s policy is represented as a classifier mapping states to actions....
Lihong Li, Vadim Bulitko, Russell Greiner
ATAL
2009
Springer
14 years 2 months ago
Solving multiagent assignment Markov decision processes
We consider the setting of multiple collaborative agents trying to complete a set of tasks as assigned by a centralized controller. We propose a scalable method called“Assignmen...
Scott Proper, Prasad Tadepalli
AR
2007
105views more  AR 2007»
13 years 7 months ago
Reinforcement learning of a continuous motor sequence with hidden states
—Reinforcement learning is the scheme for unsupervised learning in which robots are expected to acquire behavior skills through self-explorations based on reward signals. There a...
Hiroaki Arie, Tetsuya Ogata, Jun Tani, Shigeki Sug...