  With dynamic pricing on the rise, firms are using sophisticated algorithms
for price determination. These algorithms are often non-interpretable and there
has been a recent interest in their seemingly emergent ability to tacitly
collude with each other without any prior communication whatsoever. Most of the
previous works investigate algorithmic collusion on simple reinforcement
learning (RL) based algorithms operating on a basic market model. Instead, we
explore the collusive tendencies of Proximal Policy Optimization (PPO), a
state-of-the-art continuous state/action space RL algorithm, on a complex
double-sided hierarchical market model of rideshare. For this purpose, we
extend a mathematical program network (MPN) based rideshare model to a temporal
multi origin-destination setting and use PPO to solve for a repeated duopoly
game. Our results indicate that PPO can either converge to a competitive or a
collusive equilibrium depending upon the underlying market characteristics,
even when the hyper-parameters are held constant.
