Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Inductive reasoning and bounded rationality reconsidered
IEEE Transactions on Evolutionary Computation
Agent-based modelling of product invention
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Simulating sellers in online exchanges
Decision Support Systems
Simulating sellers in online exchanges
Decision Support Systems
Simulating sellers' behavior in a reverse auction B2B exchange
ICCS'03 Proceedings of the 2003 international conference on Computational science
A Class of Evolutionary Models for Participation Games with Negative Feedback
Computational Economics
International Journal of Agent Technologies and Systems
| Hi-index | 0.00 |
This paper examines the performance of simple reinforcement learningalgorithms in a stationary environment and in a repeated game where theenvironment evolves endogenously based on the actions of other agents. Sometypes of reinforcement learning rules can be extremely sensitive to smallchanges in the initial conditions, consequently, events early in a simulationcan affect the performance of the rule over a relatively long time horizon.However, when multiple adaptive agents interact, algorithms that performedpoorly in a stationary environment often converge rapidly to a stableaggregate behaviors despite the slow and erratic behavior of individuallearners. Algorithms that are robust in stationary environments can exhibitslow convergence in an evolving environment.