Learning against learning : evolutionary dynamics of reinforcement learning algorithms in strategic interactions

Imagine computer programs (agents) that learn to coordinate or to compete. This study investigates how their learning processes influence each other. Such adaptive agents already take vital roles behind the scenes of our society, e.g., high frequency automated traders participate in financial trading and create more volume than human trading in some US markets. However, many learning algorithms only have proven performance guarantees if they act alone - as soon as a second agent influences the outcomes most guarantees are invalid. This dissertation extends guarantees to strategic interactions of several agents and examines how closely algorithms approximate optimal behavior.

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