SARSA state-action-reward-state-action

SARSA (State-Action-Reward-State-Action) is a reinforcement learning algorithm that is commonly used to solve sequential decision-making problems. It is an on-policy algorithm, meaning that it learns the optimal policy by directly interacting with the environment and updating its estimates based on the actions it takes.

The SARSA algorithm operates by estimating the values of state-action pairs (Q-values). These values represent the expected future rewards that an agent can achieve by taking a particular action in a specific state. The algorithm uses these value estimates to make decisions about which actions to take in a given state.

Let's break down the components of SARSA:

1. State (S): A state represents the current situation or configuration of the environment. It captures all the relevant information that the agent needs to make decisions. In SARSA, the agent observes the current state, and based on that, selects an action to take.
2. Action (A): An action is a specific move or decision that the agent can take in a given state. The set of possible actions depends on the environment. In SARSA, the agent selects an action based on its current policy, which is typically an exploration-exploitation strategy like epsilon-greedy.
3. Reward (R): After the agent takes an action in a given state, it receives a reward from the environment. The reward represents the immediate feedback that the agent gets for its action. It can be positive, negative, or zero, depending on the outcome of the action. The goal of the agent is to maximize the cumulative reward over time.
4. Next State (S'): After the agent takes an action in a state, the environment transitions to a new state. This new state is denoted as S' and represents the state that the agent will be in after taking the action.

The SARSA algorithm updates its Q-value estimates based on the observed state-action-reward-state-action transitions. The update rule is as follows:

Q(S, A) ← Q(S, A) + α * [R + γ * Q(S', A') - Q(S, A)]

where:

• Q(S, A) is the current estimate of the Q-value for state S and action A.
• α (alpha) is the learning rate, determining how much the algorithm updates its estimates based on new information. It is a value between 0 and 1.
• R is the reward received after taking action A in state S.
• γ (gamma) is the discount factor, which determines the importance of future rewards. It is a value between 0 and 1.
• Q(S', A') is the Q-value estimate for the next state-action pair. A' represents the action selected in the next state S'.

The SARSA algorithm proceeds by iteratively updating the Q-values based on the observed transitions, exploring the environment, and gradually improving its policy. The process continues until the agent reaches the terminal state or a predefined stopping criterion.

Overall, SARSA is an on-policy algorithm that learns the Q-values by directly interacting with the environment, updating its estimates based on the observed state-action-reward-state-action transitions. It aims to find the optimal policy by balancing exploration and exploitation and maximizing the cumulative reward over time.