Q Learning

Q-learning is a popular reinforcement learning algorithm that is used to learn the optimal action-selection policy for a given system. The goal of Q-learning is to find the best action to take in a given state, so as to maximize the expected cumulative reward over time.

The algorithm is based on the Q-function, which is a function that maps a state-action pair to a scalar value. The Q-function represents the expected cumulative reward of taking a specific action in a specific state, and following the optimal policy thereafter. The optimal Q-function is defined as the maximum of the Q-function over all possible actions.

To find the optimal Q-function, Q-learning uses a process of trial and error. The agent interacts with the environment, and at each step, it selects an action based on the current estimate of the Q-function. After taking the action, the agent receives a reward and observes the next state. The agent then updates its estimate of the Q-function using the observed reward and the next state.

The Q-learning algorithm uses the Bellman equation to update the Q-function. The Bellman equation expresses the Q-function as a function of the current state, the action taken, the reward received, and the Q-function of the next state. The Bellman equation is used to update the Q-function, so that it converges to the optimal Q-function over time.

One of the key advantages of Q-learning is that it can be used for both discrete and continuous action spaces. It can also be used in both deterministic and non-deterministic environments. However, Q-learning can have difficulty converging in large or continuous state spaces. In these cases, function approximation techniques can be used to approximate the Q-function.

In summary, Q-learning is a powerful and versatile reinforcement learning algorithm that is used to find the optimal action-selection policy for a given system. It uses the Q-function, which represents the expected cumulative reward of taking a specific action in a specific state, and following the optimal policy thereafter. Q-learning updates its estimates of the Q-function using the Bellman equation and it can be used for both discrete and continuous action spaces. However, it can have difficulty converging in large or continuous state spaces, in these cases function approximation can be used to approximate the Q-function.