Event Ticket Dynamic Pricing Strategy Simulator

Develop a sophisticated Python-based dynamic pricing simulator for entertainment events using reinforcement learning techniques. Create a comprehensive model that considers factors like historical ticket sales, real-time demand curves, competitor pricing, artist popularity, and venue capacity. Implement a Q-learning algorithm that can generate optimal pricing strategies and simulate potential revenue scenarios. The solution should include Monte Carlo simulations and provide visualizations of potential pricing outcomes.