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Advantages of cooperation between reinforcement learning agents in difficult stochastic problems (August 2000)
This paper presents the first results in understanding the reasons for cooperative advantage between reinforcement learning agents. We consider a cooperation method which consists of using and updating a common policy. We tested this method on a complex fuzzy reinforcement learning problem and found that cooperation brings larger than expected benefits. More precisely, we found that K cooperative agents each learning for N time steps outperform K independent agents each learning in a separate world for K*N time steps. We explain the observed phenomenon and determine the necessary conditions for its presence in a wide class of reinforcement learning problems.
Hamid R. Berenji, David Vengerov
Advantages of cooperation between reinforcement learning agents in difficult stochastic problems 2000.pdf
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