MATLAB Implementation of Q-Learning Algorithm with Code Description

Q-learning is a classic reinforcement learning algorithm particularly suitable for solving Markov Decision Process (MDP) problems. Implementing Q-learning in MATLAB helps us better understand the decision optimization process of intelligent agents, especially when performing cooperative or competitive tasks in multi-agent environments. ### Fundamental Concepts of Q-Learning The core of Q-learning is the Q-table, which is a state-action pair matrix storing the expected reward values for an agent taking different actions in each state. Through continuous iterative updates of the Q-table, the agent can gradually optimize its decision-making strategy. In MATLAB implementation, the Q-table is typically initialized as a zeros matrix using: Q = zeros(num_states, num_actions). ### Q-Learning in Multi-Agent Environments In multi-agent systems, each agent can maintain its own Q-table or share a common Q-table. For competitive tasks (such as games), agents need to independently learn optimal strategies; for cooperative tasks (such as path planning), agents can accelerate learning by sharing experiences (like Q-values). The MATLAB implementation often involves creating multiple Q-tables or designing shared update mechanisms. ### Steps for Implementing Q-Learning in MATLAB 1. Initialize Q-table: Create a zero matrix where rows correspond to state numbers and columns correspond to action numbers, implemented as Q = zeros(S, A) 2. Set learning parameters: Include learning rate (α), discount factor (γ), and exploration rate (ε) to balance exploration and exploitation 3. Select action: Determine the next action based on the current Q-table and ε-greedy policy using conditional statements like if rand() < ε; action = random; else; [~, action] = max(Q(state,:)); end 4. Execute action and observe reward: After the agent executes the action, the environment returns a new state and immediate reward, typically handled through environment simulation functions 5. Update Q-table: Adjust Q-values using the Bellman equation: Q(state,action) = Q(state,action) + α * (reward + γ * max(Q(new_state,:)) - Q(state,action)) 6. Iterate until convergence: Repeat the above steps until the Q-table stabilizes or reaches the preset number of iterations, usually implemented using for/while loops with convergence checks ### Q-Table Output and Analysis In MATLAB, the final Q-table can intuitively reflect the agent's strategy. For example, in path planning problems, the Q-table shows the optimal moving direction in each state; in multi-agent systems, comparing Q-tables of different agents can analyze their decision differences. MATLAB's visualization tools like surf() or imagesc() can help display Q-table distributions. ### Application Value MATLAB's matrix operation capabilities make Q-learning implementation highly efficient, particularly suitable for researching multi-agent reinforcement learning problems. By adjusting learning parameters, we can observe Q-learning's adaptability in different environments, such as robot control, game AI, or autonomous driving fields. Although Q-learning is representative of single-agent algorithms, its variants like Independent Q-Learning (IQL) or Collaborative Q-Learning (CQL) can extend to multi-agent scenarios, providing powerful support for intelligent system training. MATLAB's object-oriented programming capabilities facilitate the implementation of these advanced variants.