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AROBOTS
1999
104views more  AROBOTS 1999»
13 years 5 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...
ICML
2006
IEEE
14 years 6 months ago
Learning the structure of Factored Markov Decision Processes in reinforcement learning problems
Recent decision-theoric planning algorithms are able to find optimal solutions in large problems, using Factored Markov Decision Processes (fmdps). However, these algorithms need ...
Thomas Degris, Olivier Sigaud, Pierre-Henri Wuille...
ATAL
2007
Springer
13 years 12 months ago
IFSA: incremental feature-set augmentation for reinforcement learning tasks
Reinforcement learning is a popular and successful framework for many agent-related problems because only limited environmental feedback is necessary for learning. While many algo...
Mazda Ahmadi, Matthew E. Taylor, Peter Stone
IROS
2007
IEEE
100views Robotics» more  IROS 2007»
13 years 12 months ago
Incremental behavior acquisition based on reliability of observed behavior recognition
— We propose a novel approach for acquisition and development of behaviors through observation in multi-agent environment. Observed behaviors of others give fruitful hints for a ...
Tomoki Nishi, Yasutake Takahashi, Minoru Asada
ICML
1998
IEEE
14 years 6 months ago
The MAXQ Method for Hierarchical Reinforcement Learning
This paper presents a new approach to hierarchical reinforcement learning based on the MAXQ decomposition of the value function. The MAXQ decomposition has both a procedural seman...
Thomas G. Dietterich