1 research outputs found
Statistical-mechanics approach to a reinforcement learning model with memory
We introduce a two-player model of reinforcement learning with memory. Past
actions of an iterated game are stored in a memory and used to determine
player's next action. To examine the behaviour of the model some approximate
methods are used and confronted against numerical simulations and exact master
equation. When the length of memory of players increases to infinity the model
undergoes an absorbing-state phase transition. Performance of examined
strategies is checked in the prisoner' dilemma game. It turns out that it is
advantageous to have a large memory in symmetric games, but it is better to
have a short memory in asymmetric ones.Comment: 6 pages, some additional numerical calculation