Multi-Agent Pricing Implementation with Dual Q-Learner Agents

This code implements multi-agent pricing between two Q-Learner agents using a reinforcement learning approach. The implementation enables agents to learn optimal pricing strategies through environmental interactions. The core algorithm employed is Q-Learning, a reinforcement learning method based on the Bellman equation, where each agent maintains a Q-table to store learned knowledge about state-action pairs and their corresponding expected rewards. The implementation features state space definition for pricing scenarios, action selection using ε-greedy policy for exploration-exploitation balance, and reward calculation based on market responses. Key functions include Q-value updates using the temporal difference formula: Q(s,a) ← Q(s,a) + α[r + γmaxQ(s',a') - Q(s,a)], where α represents the learning rate and γ the discount factor. This demonstration shows how multi-agent pricing problems can be effectively solved through reinforcement learning. The approach allows agents to continuously refine their pricing strategies during the learning process, leading to improved outcomes. The Q-Learning algorithm enables agents to accumulate knowledge through repeated interactions, enhancing their decision-making capabilities. This serves as an excellent example of reinforcement learning applications in multi-agent systems, particularly for dynamic pricing optimization in competitive environments.