Differences between Q-learning and SARSA

Q-learning and SARSA (State-Action-Reward-State-Action) are reinforcement learning algorithm used to find the optimal policy in a Markov Decision Process (MDP). Both are value-based methods that learn action-value functions but they differ how they update their Q-values and handle the balance between exploration and exploitation. This article explains the key differences between Q-learning and SARSA in terms of their learning approach and update rules.

1. Policy Type: Off-policy vs On-policy

• Q-learning is an off-policy method meaning it learns the best strategy without depending on the agent’s actual actions. It updates its values using the highest possible reward from the next state no matter what the agent really did.
• SARSA is an on-policy method meaning it updates its values based on the actions the agent actually takes. It follows the current behavior so the agent’s real choices in the next state affect how learning

2. Update Rule

The way each algorithm updates the Q-values distinguishes them:

• Q-learning Update Rule:

Q(s_t, a_t) \leftarrow Q(s_t, a_t) + \alpha \left( r_{t+1} + \gamma \max_{a'} Q(s_{t+1}, a') - Q(s_t, a_t) \right)

In Q-learning the update is based on the maximum Q-value of the next state which is the highest possible reward the agent could achieve from the next state, independent of the action actually chosen.

• SARSA Update Rule:

Q(s_t, a_t) \leftarrow Q(s_t, a_t) + \alpha \left( r_{t+1} + \gamma Q(s_{t+1}, a_{t+1}) - Q(s_t, a_t) \right)

In SARSA the Q-value is updated based on the actual action taken in the next state. This means the learning process depends on both the next state and the action chosen using the current strategy.

3. Exploration and Exploitation:

• Q-learning focuses on finding the highest possible reward by assuming the best future action will be taken. While this helps in exploring new possibilities it may sometimes overestimate the value of actions.
• SARSA follows a more cautious approach. It learns from the actual actions taken based on the current strategy. This helps to avoid overestimating action values and keeps learning more in line with how the system is behaving.

4. Convergence and Learning Speed:

• Q-learning tends to learn faster because it always looks ahead to the best possible outcome even if that’s not what the current strategy would do. This makes it effective in situations where fast and broad exploration is helpful.
• SARSA may learn more slowly since it updates based on the actions it actually takes. However this makes it more stable and consistent especially when the strategy needs to be followed closely or when too much exploration could cause problems.

5. Suitability:

• Q-learning works best in situations where the goal is to find the most effective strategy as quickly as possible and there's room to try out many different actions without much risk.
• SARSA is better suited to situations where actions need to stay within a certain strategy and where keeping learning steady and controlled is more important than chasing the best outcome right away.

Differences between Q-learning and SARSA

The choice between these algorithms depends on the problem at hand the desired exploration-exploitation balance and the stability requirements of the environment.