SARSA

SARSA (State-Action-Reward-State-Action) is a reinforcement learning algorithm that is used to find the optimal policy for a given system. It is similar to Q-learning, but it estimates the value of a policy rather than the value of an action.

Like Q-learning, SARSA uses a function, called the Q-function, to estimate the expected cumulative reward of taking a specific action in a specific state, and following the optimal policy thereafter. The Q-function is represented as a table, where each entry corresponds to a state-action pair, and the value of the entry is the expected cumulative reward for that pair.

The SARSA algorithm updates the Q-function using the following process:

• The agent selects an action based on the current state and its current estimate of the Q-function.

• The agent takes the selected action, and the environment transitions to the next state and produces a reward.

• The agent selects the next action based on the next state and its current estimate of the Q-function.

• The agent updates its estimate of the Q-function for the state-action pair (current state, current action) using the observed reward, the next state, and the next action.

The key difference between SARSA and Q-learning is that SARSA uses the next action selected according to the current policy to update the Q-values, while Q-learning uses the action that has the highest estimated value.

SARSA is an on-policy algorithm, meaning it updates the Q-function using the actions that are selected according to the current policy. This is in contrast to Q-learning, which is an off-policy algorithm, meaning it updates the Q-function using the actions that have the highest estimated value, regardless of the current policy.

SARSA is a robust algorithm and it performs well in environments with stochastic transitions and rewards. It is also less prone to over-estimating the value of an action in certain states. However, it can have a slower convergence than Q-learning in some cases, as it is based on the current policy.

In summary, SARSA is a reinforcement learning algorithm that is used to find the optimal policy for a given system. It is similar to Q-learning, but it estimates the value of a policy rather than the value of an action. The algorithm updates the Q-function by taking into account the next action selected according to the current policy, making it an on-policy algorithm. SARSA is a robust algorithm that performs well in environments with stochastic transitions and rewards, but it can have a slower convergence than Q-learning in some cases.