This paper covers the use of a genetic algorithm to create ideal Q-learning parameters for the creation of a Q-table used by a simulated mobile agent for object avoidance. An arena simulation was created that would handle the agent, as well as calculating its Q-table. Compared to a realworld environment, evaluation proceeded much faster, with a 40-minute experiment reduced to some 6 seconds in the simulation. Despite this speed increase, the simulation was too slow to definitively answer the question of whether ideal Q-table parameters can be evolved. Results suggest that the evolved parameters are highly specified, according to the configuration of the arena
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