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IROS
2006
IEEE
113views Robotics» more  IROS 2006»
14 years 3 months ago
Policy Gradient Methods for Robotics
— The aquisition and improvement of motor skills and control policies for robotics from trial and error is of essential importance if robots should ever leave precisely pre-struc...
Jan Peters, Stefan Schaal
ECML
2005
Springer
14 years 2 months ago
Natural Actor-Critic
This paper investigates a novel model-free reinforcement learning architecture, the Natural Actor-Critic. The actor updates are based on stochastic policy gradients employing Amari...
Jan Peters, Sethu Vijayakumar, Stefan Schaal
ATAL
2008
Springer
13 years 11 months ago
Expediting RL by using graphical structures
The goal of Reinforcement learning (RL) is to maximize reward (minimize cost) in a Markov decision process (MDP) without knowing the underlying model a priori. RL algorithms tend ...
Peng Dai, Alexander L. Strehl, Judy Goldsmith
AAAI
2006
13 years 10 months ago
Modeling Human Decision Making in Cliff-Edge Environments
In this paper we propose a model for human learning and decision making in environments of repeated Cliff-Edge (CE) interactions. In CE environments, which include common daily in...
Ron Katz, Sarit Kraus
ICML
2010
IEEE
13 years 7 months ago
Temporal Difference Bayesian Model Averaging: A Bayesian Perspective on Adapting Lambda
Temporal difference (TD) algorithms are attractive for reinforcement learning due to their ease-of-implementation and use of "bootstrapped" return estimates to make effi...
Carlton Downey, Scott Sanner