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This paper presents a novel algorithm of Multiagent Reinforcement Learning called State Elimination in Accelerated Multiagent Reinforcement Learning (SEA-MRL), that successfully produces faster learning without incorporating internal knowledge or human intervention such as reward shaping, transfer learning, parameter tuning, and even heuristics, into the learning system. Since the learning speed is determined among others by the size of the state space where the larger the state space thedoi:10.15676/ijeei.2016.8.3.12 fatcat:aydhzofht5hfhlypek4xkterpq