Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Learning to Be Thoughtless: Social Norms and Individual Computation
Computational Economics
Multiagent and Grid Systems
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We propose a model of vicarious reinforcement in rule-based learning agents. The influence of this reinforcement is investigated in a population where a law is enforced ex ante. The norm-governed population of learning agents is formalised and simulated in an executable probabilistic rule-based argumentation framework. Vicarious experiences are expressed with rules and their learning effects are integrated into reinforcement learning. So, agents learn not only from their own experiences but also by taking into account the experiences of others. We show that simulation results differ from traditional calculus based on expected utilities.