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ATAL
2008
Springer

Non-linear dynamics in multiagent reinforcement learning algorithms

8 years 4 months ago
Non-linear dynamics in multiagent reinforcement learning algorithms
Several multiagent reinforcement learning (MARL) algorithms have been proposed to optimize agents' decisions. Only a subset of these MARL algorithms both do not require agents to know the underlying environment and can learn a stochastic policy (a policy that chooses actions according to a probability distribution). Weighted Policy Learner (WPL) is a MARL algorithm that belongs to this subset and was shown, experimentally in previous work, to converge and outperform previous MARL algorithms belonging to the same subset. The main contribution of this paper is analyzing the dynamics of WPL and showing the effect of its non-linear nature, as opposed to previous MARL algorithms that had linear dynamics. First, we represent the WPL algorithm as a set of differential equations. We then solve the equations and show that it is consistent with experimental results reported in previous work. We finally compare the dynamics of WPL with earlier MARL algorithms and discuss the interesting dif...
Sherief Abdallah, Victor R. Lesser
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2008
Where ATAL
Authors Sherief Abdallah, Victor R. Lesser
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