Multi-Agent Learning in Dynamic Pricing Games of Service Markets

We study price dynamics in a service market environment where identical service providers dynamically reset their prices to price discriminate informed and uninformed consumers. A semi-Markovian game model for dynamic pricing is developed and a new multi-time scale actor-critic algorithm is proposed for multi-agent reinforcement learning. Also, experimental results on convergence to a Nash equilibrium are presented.