何时选择SARSA算法与Q Learning算法？

Hey there! Let's break down the key distinctions between SARSA and Q-Learning—two reinforcement learning algorithms that work almost identically, but have one defining difference in their policy approach:

• SARSA: This is an on-policy algorithm. That means it learns using the exact same policy it uses to explore the environment. When updating Q-values, it relies on the action it actually took next (not just the best possible action).
• Q-Learning: This is an off-policy algorithm. Here, the learning process uses a separate policy from the one it uses to act. It updates Q-values based on the optimal possible next action (the one with the highest Q-value for the next state), even if it didn't actually take that action during exploration.

Update Rules #

Let's look at their formal update equations to see this difference in action:

Q-Learning Update

$$Q(s_t,a_t)←Q(s_t,a_t)+α[r_{t+1}+γ\max_{a'}Q(s_{t+1},a')−Q(s_t,a_t)]$$

SARSA Update

$$Q(s_t,a_t)←Q(s_t,a_t)+α[r_{t+1}+γQ(s_{t+1},a_{t+1})−Q(s_t,a_t)]$$

What Each Symbol Means #

• $s_t$: The state the agent is in at time step $t$
• $a_t$: The action the agent takes at time step $t$
• $r_t$: The reward the agent receives at time step $t$
• $\alpha$: Learning rate (determines how much we adjust Q-values with each new experience)
• $\gamma$: Discount factor (balances the importance of immediate rewards vs. future rewards)

At a glance, these formulas are nearly identical. The only meaningful change is in the future reward term: Q-Learning takes the maximum Q-value available in the next state (a greedy choice), while SARSA uses the Q-value of the actual action the agent took next (sticking to its current exploration/exploitation policy).

内容的提问来源于stack exchange，提问作者hh32