Q-learning is a popular reinforcement learning algorithm used for solving Markov decision processes (MDPs). It is a model-free, value-based approach that enables an agent to learn an optimal policy through trial and error.
In Q-learning, the agent learns to make decisions by maintaining a Q-value table, also known as a Q-table. The Q-values represent the expected future rewards for taking specific actions in specific states. The agent updates the Q-values based on the rewards received and the Q-values of the next state.
Here’s an overview of the Q-learning process:
Initialization: Initialize the Q-table with zeros or random values for all state-action pairs.
Exploration and Exploitation: The agent interacts with the environment by taking action. This process balances exploration and exploitation to learn and maximize rewards. Exploration involves randomly selecting actions to discover new states and learn more about the environment. Exploitation involves selecting actions based on the Q-values to maximize the expected reward.
Action Selection: The agent selects an action based on an exploration-exploitation trade-off strategy, such as epsilon-greedy. With a certain probability (epsilon), the agent chooses a random action for exploration. Otherwise, it selects the action with the highest Q-value for exploitation.
Observation and Reward: The agent performs the selected action and observes the new state and the reward received from the environment.
Q-value Update: The agent updates the Q-value of the previous state-action pair using the following formula:
Q(s, a) = Q(s, a) + α * (r + γ * max(Q(s’, a’)) – Q(s, a))
Q(s, a): Q-value of state-action pair (s, a)
α (alpha): Learning rate that determines the influence of new information. It controls the rate at which the Q-values are updated.
r: Reward received for taking action an in state s
γ (gamma): Discount factor that balances immediate and future rewards. It determines the importance of future rewards compared to immediate rewards.
s’: Next state observed after taking action an
a’: Action selected based on the highest Q-value in the next state s’
The Q-value update equation gradually adjusts the Q-values based on the observed rewards and the expected future rewards. Over time, the Q-values converge to the optimal values that correspond to the best policy.
Repeat: Steps 3 to 5 are repeated until the agent reaches a termination condition, such as a specific number of iterations or convergence of Q-values.
Q-learning continues to update the Q-values iteratively, exploring and exploiting the environment to find the optimal policy. The learned Q-values guide the agent’s decision-making process, allowing it to select actions that maximize the cumulative rewards over time.
Q-learning is a fundamental algorithm in reinforcement learning and has been successfully applied to various problems, including game-playing, robotics, and control systems. It offers a way for agents to learn optimal policies in environments with unknown dynamics and provides a foundation for more advanced algorithms in reinforcement learning.
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