Overview of Sarsa Algorithm in Reinforcement Learning
Reinforcement learning is a type of machine learning that focuses on predicting what actions to take in a specific situation based on feedback from the environment. One algorithm in reinforcement learning is Sarsa, which stands for State-Action-Reward-State-Action. It is an on-policy TD (Temporal Difference) control algorithm that updates the Q-value for every transition from a non-terminal state.
How Sarsa Works
In Sarsa, the goal is to estimate the optimal State-Action value, also known as Q-value, for a given policy. Q-values represent how much reward is expected to be gained by taking a specific action in a specific state. The algorithm uses Q-learning, which relies on an iterative process of improving the estimation of the optimal Q-value for each possible state-action pair.
The Sarsa algorithm estimates the Q-value based on the following formula:
Q(St, At) << Q(St, At) + α(Rt+1 + γQ(St+1, At+1) - Q(St, At))
where:
- α is the learning rate that adjusts how quickly the algorithm accumulates new information.
- Rt+1 is the reward received after taking action At in state St.
- γ is the discount factor that determines the relative importance of future rewards.
- Q(St+1, At+1) is the estimated Q-value of the next state-action pair.
On-policy TD Control
Sarsa is an on-policy control algorithm. This means that the policy that is being updated during training is the same policy that is used to generate actions during training. In contrast, off-policy algorithms update the Q-values based on a different policy than the one that is being followed during training.
Updating the policy during training means that the algorithm uses exploration and exploitation strategies to learn about the environment in a more efficient way. The exploration phase selects actions to discover new states, while the exploitation phase selects actions based on the current policy. In Sarsa, the exploitation phase involves estimating the optimal Q-value for each state-action pair and selecting the action with the highest Q-value.
Designing an On-policy Control Algorithm
To design an on-policy control algorithm using Sarsa, we first estimate the Q-value for a behavior policy π and then update π towards greediness with respect to the estimated Q-values. The goal of this process is to learn the optimal policy that maximizes the expected cumulative reward.
The behavior policy is the initial policy that is used to generate actions. The algorithm learns about the environment by observing the results of these actions and updates the policy based on the feedback received. This means that the policy becomes more informed as the algorithm accumulates more experience in the environment.
Once the policy has been updated, the algorithm repeats the process of estimating the Q-value for the updated policy and using it to update the policy. This iterative process continues until the algorithm converges to the optimal policy.
Sarsa is an on-policy control algorithm used in reinforcement learning that estimates the optimal State-Action value for a given policy. It iteratively improves the estimation of the Q-value for every transition from a non-terminal state, updating the policy towards greediness with respect to the estimated Q-values. By learning the optimal policy that maximizes the expected cumulative reward, Sarsa is able to solve a wide range of reinforcement learning problems.
