Multi-Agent Pricing Implementation with Dual Q-Learning Agents

Implementing Q-Learning-based pricing strategies in multi-agent systems represents an intriguing intersection of game theory and machine learning. This system simulates two merchants selling homogeneous products that dynamically adjust prices through reinforcement learning. The system operates through the following components: Each merchant functions as an independent Q-Learning agent maintaining its own Q-value table The state space typically incorporates environmental factors such as current market conditions and inventory levels The action space consists of possible pricing strategies The reward function is based on business metrics like sales revenue and profit margins Key implementation considerations in the learning process include: Balancing exploration versus exploitation, particularly in dynamic environments Managing mutual influence between the two agents' learning processes Accounting for convergence sensitivity to initial conditions and learning parameters The advantages of this approach lie in its adaptability to market changes without requiring prior knowledge of competitors' strategies. Through repeated interactions, agents gradually optimize their pricing strategies, potentially reaching an equilibrium state. In practical applications, this method can be extended to more complex market environments with additional participants. Code Implementation Notes: - Agents typically use epsilon-greedy policies for action selection - Q-table updates follow the standard formula: Q(s,a) = Q(s,a) + α[r + γmaxQ(s',a') - Q(s,a)] - State representation may require discretization of continuous variables - Reward function design crucially impacts learning behavior and outcomes