reinforcement learning models than with belief-based models. Showing level-2 rationality (i.e., reasoning about others' beliefs) is … process level to the validity of modeling behavior using low-rationality reinforcement learning models. …

. The results illustrate that simple reinforcement learning is sufficient to detect correlation between investments. …

reinforcement learning models than with belief-based models. Showing level-2 rationality (i.e., reasoning about others' beliefs) is … process level to the validity of modeling behavior using low-rationality reinforcement learning models. …

reinforcement learning models than with belief-based models. Showing level-2 ratio- nality (i.e., reasoning about others' beliefs … the decision process level to the validity of modeling behavior using low-rationality reinforcement learning models. …

principle of reinforcement learning, and does not require parametric assumptions on either the agents' information set, or on …

This paper studies the analytical properties of the reinforcement learning model proposed in Erev and Roth (1998), also … termed cumulative reinforcement learning in Laslier et al (2001). This stochastic model of learning in games accounts for two … close to one, all the realizations of the reinforcement learning process will, from some time on, lie within an " band of …

choice probabilities. Our results are completed by proving that there exists no quasi-linear reinforcement learning …

function under reinforcement learning model. In doing so, I have considered the spatio-temporal environment under bounded …

While a number of algorithms for multiobjective reinforcement learning have been proposed, and a small number of … studies. Two classes of multiobjective reinforcement learning algorithms are identified, and appropriate evaluation metrics …