Sarsa is an on-policy reinforcement learning algorithm used to update the action-value function based on the agent's current state, the action taken, the reward received, and the next state and action chosen. It stands for State-Action-Reward-State-Action and is particularly known for balancing exploration and exploitation while learning an optimal policy. The algorithm continually updates its estimates based on actions actually taken, which makes it distinct from off-policy methods like Q-learning.
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