3 research outputs found
Scale-free memory model for multiagent reinforcement learning. Mean field approximation and rock-paper-scissors dynamics
A continuous time model for multiagent systems governed by reinforcement
learning with scale-free memory is developed. The agents are assumed to act
independently of one another in optimizing their choice of possible actions via
trial-and-error search. To gain awareness about the action value the agents
accumulate in their memory the rewards obtained from taking a specific action
at each moment of time. The contribution of the rewards in the past to the
agent current perception of action value is described by an integral operator
with a power-law kernel. Finally a fractional differential equation governing
the system dynamics is obtained. The agents are considered to interact with one
another implicitly via the reward of one agent depending on the choice of the
other agents. The pairwise interaction model is adopted to describe this
effect. As a specific example of systems with non-transitive interactions, a
two agent and three agent systems of the rock-paper-scissors type are analyzed
in detail, including the stability analysis and numerical simulation.
Scale-free memory is demonstrated to cause complex dynamics of the systems at
hand. In particular, it is shown that there can be simultaneously two modes of
the system instability undergoing subcritical and supercritical bifurcation,
with the latter one exhibiting anomalous oscillations with the amplitude and
period growing with time. Besides, the instability onset via this supercritical
mode may be regarded as "altruism self-organization". For the three agent
system the instability dynamics is found to be rather irregular and can be
composed of alternate fragments of oscillations different in their properties.Comment: 17 pages, 7 figur