Ascending the mountain: collaborative reinforcement learning between agents with opposing goals
DOI:
https://doi.org/10.17979/ja-cea.2026.47.13783Keywords:
Artificial Intelligence, Reinforcement Learning, OptimizationAbstract
This work explores the concept of Artificial Stupidity as a complement to Artificial Intelligence in the context of reinforcement learning. To this end, a series of experiments are conducted using the Q-Learning algorithm in the Mountain Car environment, comparing an intelligent agent with a stupid agent that exhibits opposite behaviors. Two training strategies are proposed: a reformative one, where the intelligent agent is trained from the stupid agent, and a collaborative one, where both are trained simultaneously by sharing information. The results show that incorporating information from the stupid agent improves the performance of the intelligent agent, accelerating its convergence and enhancing its overall performance.
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Copyright (c) 2026 Violeta Tejera-Munguía, Juan Jesús Roldán-Gómez, José Luis Jorro-Aragoneses
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