1️⃣Q-Learning
Q-Learning is a model-free, off-policy reinforcement learning algorithm that is used to find the optimal action-value function, Q(s,a), for an agent in a given state, s, and taking a specific action, a. The optimal action-value function represents the maximum expected long-term reward an agent can receive by taking a specific action in a given state.
The Q-function is updated using the Bellman equation, which expresses the Q-function of a state and an action in terms of the Q-function of the next state and the reward received for taking that action. The Bellman equation is defined as:
where r is the immediate reward received after taking action a in state s, γ is the discount factor, a' is the action taken in the next state s', and max(Q(s',a')) is the maximum expected long-term reward the agent can receive in the next state s' by taking any action a'.
The agent uses the Q-function to choose the action that maximizes the expected long-term reward in each state. This is known as the greedy action policy.
The Q-Learning algorithm starts with an arbitrary Q-function, and updates it using the Bellman equation as the agent interacts with the environment. The update rule is defined as:
where α is the learning rate, that determines how much the agent relies on the new information.
Analogy:
A good analogy for Q-Learning is to think of it as a way for an agent to learn the best strategy for a game, by trying different moves, and updating its strategy based on the rewards it receives. For example, imagine a chess-playing agent that starts with a random strategy, and gradually improves its strategy by playing against itself and learning from its mistakes. Each time the agent makes a move, it receives a reward based on the outcome of the game, and it updates its strategy based on this reward and the next state of the game.
Q-learning is a powerful algorithm and it's widely used in many applications, such as robotics, control systems, and gaming. However, it's worth noting that it's sensitive to the choice of the learning rate, discount
Example
In this example, the agent uses a Q-table to represent the Q-function, and updates it using the Bellman equation. The agent selects actions using an exploration-exploitation strategy, where it chooses a random action with a probability of epsilon, and the action that maximizes the Q-value with a probability of 1-epsilon. The select_action
and take_action
functions would need to be implemented for the specific problem you are trying to solve.
It's important to note that Q-Learning is a model-free algorithm, it doesn't require a model of the environment. Q-Learning is also an off-policy algorithm, it doesn't need to follow the current policy during learning process. This allows Q-Learning to learn from any experience it has, even if it's not following the optimal policy.
Python code
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