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ATAL
2007
Springer
14 years 4 months ago
Reducing the complexity of multiagent reinforcement learning
It is known that the complexity of the reinforcement learning algorithms, such as Q-learning, may be exponential in the number of environment’s states. It was shown, however, th...
Andriy Burkov, Brahim Chaib-draa
ECML
2007
Springer
14 years 4 months ago
Imitation Learning Using Graphical Models
Imitation-based learning is a general mechanism for rapid acquisition of new behaviors in autonomous agents and robots. In this paper, we propose a new approach to learning by imit...
Deepak Verma, Rajesh P. N. Rao
ROBOCUP
2007
Springer
153views Robotics» more  ROBOCUP 2007»
14 years 4 months ago
Model-Based Reinforcement Learning in a Complex Domain
Reinforcement learning is a paradigm under which an agent seeks to improve its policy by making learning updates based on the experiences it gathers through interaction with the en...
Shivaram Kalyanakrishnan, Peter Stone, Yaxin Liu
GECCO
2005
Springer
174views Optimization» more  GECCO 2005»
14 years 3 months ago
Emergence of communication in competitive multi-agent systems: a pareto multi-objective approach
In this paper we investigate the emergence of communication in competitive multi-agent systems. A competitive environment is created with two teams of agents competing in an explo...
Michelle McPartland, Stefano Nolfi, Hussein A. Abb...
ROBOCUP
2004
Springer
118views Robotics» more  ROBOCUP 2004»
14 years 3 months ago
Approaching Urban Disaster Reality: The ResQ Firesimulator
The RoboCupRescue Simulation project aims at simulating large-scale disasters in order to explore coordination strategies helpful for real-life rescue missions. This can only be ac...
Timo A. Nüssle, Alexander Kleiner, Michael Br...