Q-learning and SARSA are both popular algorithms used in reinforcement learning, a subfield of machine learning focused on teaching agents to make optimal decisions through trial and error. These algorithms are designed to help an agent learn how to act in an environment in order to maximize some notion of cumulative reward. While both approaches share the same goal, they differ fundamentally in how they update their value functions and make decisions.
At the core of both Q-learning and SARSA is a Q-value, which estimates the expected future rewards that an agent can obtain from a given state-action pair. The primary difference between the two algorithms lies in how they update these Q-values.
Q-learning is an off-policy algorithm, meaning it learns the value of the optimal policy independently of the agent’s actions. It updates its Q-values using the maximum reward of the next state, assuming that the best possible action is always taken. This approach allows Q-learning to converge to the optimal policy even if the agent is exploring suboptimal actions during training. The update rule in Q-learning is based on the difference between the current Q-value and the sum of the observed reward plus the maximum expected future reward from the next state.
In contrast, SARSA, which stands for State-Action-Reward-State-Action, is an on-policy algorithm. It updates the Q-values based on the action actually taken by the agent, rather than assuming the best possible action. This means that SARSA learns the value of the policy being followed, which may not always be optimal. The SARSA update rule considers the reward received after taking an action and the expected reward of the next action chosen by the current policy.
These differences have practical implications. Q-learning’s off-policy nature allows it to be more exploratory and robust in environments where optimal actions are known to lead to high rewards. It can be more efficient in learning the optimal policy but might be more sensitive to the exploration strategy used. SARSA, by updating based on actions taken, can be more stable in environments where the agent’s policy is constantly evolving and less aggressive in terms of exploration, as it adjusts its Q-values based on the actions it actually experiences.
In summary, choosing between Q-learning and SARSA depends on the specific requirements of your task. If you need a method that quickly converges on an optimal policy and can handle exploration independently, Q-learning may be more suitable. However, if you are working in a more dynamic environment where the policy may change frequently, SARSA’s on-policy approach could offer a more stable learning process. Understanding these fundamental differences will help you select the appropriate algorithm for your reinforcement learning applications.