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» Variational methods for Reinforcement Learning
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ICML
2005
IEEE
14 years 8 months ago
Dynamic preferences in multi-criteria reinforcement learning
The current framework of reinforcement learning is based on maximizing the expected returns based on scalar rewards. But in many real world situations, tradeoffs must be made amon...
Sriraam Natarajan, Prasad Tadepalli
ICML
1998
IEEE
14 years 8 months ago
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
In this paper, we adopt general-sum stochastic games as a framework for multiagent reinforcement learning. Our work extends previous work by Littman on zero-sum stochastic games t...
Junling Hu, Michael P. Wellman
AR
2004
84views more  AR 2004»
13 years 7 months ago
Reinforcement learning of humanoid rhythmic walking parameters based on visual information
This paper presents a method for learning the parameters of rhythmic walking to generate purposive humanoid motions. The controller consists of the two layers: rhythmic walking is...
Masaki Ogino, Yutaka Katoh, Masahiro Aono, Minoru ...
ICML
2003
IEEE
14 years 8 months ago
Relational Instance Based Regression for Relational Reinforcement Learning
Relational reinforcement learning (RRL) is a Q-learning technique which uses first order regression techniques to generalize the Qfunction. Both the relational setting and the Q-l...
Kurt Driessens, Jan Ramon
AROBOTS
1999
104views more  AROBOTS 1999»
13 years 7 months ago
Reinforcement Learning Soccer Teams with Incomplete World Models
We use reinforcement learning (RL) to compute strategies for multiagent soccer teams. RL may pro t signi cantly from world models (WMs) estimating state transition probabilities an...
Marco Wiering, Rafal Salustowicz, Jürgen Schm...