  Agent-based models (ABMs) have shown promise for modelling various real world
phenomena incompatible with traditional equilibrium analysis. However, a
critical concern is the manual definition of behavioural rules in ABMs. Recent
developments in multi-agent reinforcement learning (MARL) offer a way to
address this issue from an optimisation perspective, where agents strive to
maximise their utility, eliminating the need for manual rule specification.
This learning-focused approach aligns with established economic and financial
models through the use of rational utility-maximising agents. However, this
representation departs from the fundamental motivation for ABMs: that realistic
dynamics emerging from bounded rationality and agent heterogeneity can be
modelled. To resolve this apparent disparity between the two approaches, we
propose a novel technique for representing heterogeneous processing-constrained
agents within a MARL framework. The proposed approach treats agents as
constrained optimisers with varying degrees of strategic skills, permitting
departure from strict utility maximisation. Behaviour is learnt through
repeated simulations with policy gradients to adjust action likelihoods. To
allow efficient computation, we use parameterised shared policy learning with
distributions of agent skill levels. Shared policy learning avoids the need for
agents to learn individual policies yet still enables a spectrum of bounded
rational behaviours. We validate our model's effectiveness using real-world
data on a range of canonical $n$-agent settings, demonstrating significantly
improved predictive capability.
