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NIPS
1998
13 years 7 months ago
Gradient Descent for General Reinforcement Learning
A simple learning rule is derived, the VAPS algorithm, which can be instantiated to generate a wide range of new reinforcementlearning algorithms. These algorithms solve a number ...
Leemon C. Baird III, Andrew W. Moore
GECCO
2004
Springer
122views Optimization» more  GECCO 2004»
13 years 11 months ago
Gradient-Based Learning Updates Improve XCS Performance in Multistep Problems
This paper introduces a gradient-based reward prediction update mechanism to the XCS classifier system as applied in neuralnetwork type learning and function approximation mechani...
Martin V. Butz, David E. Goldberg, Pier Luca Lanzi
ATAL
2007
Springer
13 years 12 months ago
SMILE: Sound Multi-agent Incremental LEarning
This article deals with the problem of collaborative learning in a multi-agent system. Here each agent can update incrementally its beliefs B (the concept representation) so that ...
Gauvain Bourgne, Amal El Fallah-Seghrouchni, Henry...
ATAL
2008
Springer
13 years 7 months ago
Efficient multi-agent reinforcement learning through automated supervision
Multi-Agent Reinforcement Learning (MARL) algorithms suffer from slow convergence and even divergence, especially in large-scale systems. In this work, we develop a supervision fr...
Chongjie Zhang, Sherief Abdallah, Victor R. Lesser
ECML
2004
Springer
13 years 11 months ago
Analyzing Multi-agent Reinforcement Learning Using Evolutionary Dynamics
In this paper, we show how the dynamics of Q-learning can be visualized and analyzed from a perspective of Evolutionary Dynamics (ED). More specifically, we show how ED can be use...
Pieter Jan't Hoen, Karl Tuyls