Publication details

Payoff size variation problem in simple reinforcement learning algorithms

Authors

KVASNIČKA Michal

Year of publication 2013
Type Article in Proceedings
Conference Proceedings of the 31st International Conference Mathematical Methods in Economics 2013
MU Faculty or unit

Faculty of Economics and Administration

Citation
Field Economy
Keywords reinforcement learning; agent-based simulation; economic experiments; voluntary provision of public goods
Attached files
Description This paper shows that the speed of the reinforcement learning depends on the size of the payoffs, at least when all payoffs are positive. When the speed of learning is too fast, the agents tend to learn to play the actions which they randomly chosen in the first rounds of the learning process. The compositions of the agents’ strategies then on the aggregate level resembles the initial individual agent’s mixed strategy. This may create artificial effects in the simulations where the size of payoffs depend on the model treatments because the speed of learning cannot be tuned in.
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