What is the difference between Q-learning and SARSA?

Q-learning and SARSA are both reinforcement learning algorithms used to train agents in Markov Decision Processes, but they differ in how they update their value estimates and the policies they follow. The key distinction is that Q-learning is an off-policy algorithm, meaning it learns the optimal policy independently of the agent’s exploration behavior. In contrast, SARSA is on-policy, updating its estimates based on the actions the agent actually takes, including any exploration strategies like epsilon-greedy. This fundamental difference impacts how they handle risk, exploration, and convergence in practice.

To understand the mechanics, consider their update rules. Q-learning updates the Q-value for a state-action pair using the maximum estimated future reward from the next state, regardless of the action the agent will take next. For example, if an agent is in a grid world and moves right to a new state, Q-learning assumes the agent will take the best possible action (e.g., moving up) from that new state to calculate the target value. SARSA, however, uses the actual next action the agent takes (e.g., moving left due to exploration) to compute the update. This makes SARSA more conservative, as it incorporates the exploration strategy into its updates. If the agent occasionally takes risky actions during exploration, SARSA’s Q-values will reflect the potential penalties of those actions, whereas Q-learning might ignore them in favor of the theoretically optimal path.

The choice between Q-learning and SARSA depends on the environment and risk tolerance. Q-learning is better suited for deterministic or low-risk environments where aggressive optimization is safe. For instance, in a simple maze with no penalties for exploration, Q-learning converges faster to the optimal path. SARSA shines in risky or stochastic environments, such as a robot navigating near a cliff. If the agent might slip and fall (due to randomness), SARSA’s on-policy updates account for those exploration risks, leading to safer learned policies. Developers should prioritize Q-learning for efficiency in predictable settings and SARSA when safety during learning is critical.