"Sarsa" is a word commonly used in the Philippines, particularly for a type of traditional Filipino dance. This word is spelled using the International Phonetic Alphabet (IPA) as /sɑr.sɑ/. The "a" sound in this word is pronounced as an open back unrounded vowel in the IPA, while the "s" sounds are realized as voiceless alveolar fricatives. The spelling of "Sarsa" reflects its pronunciation in Filipino, which is a language known for its extensive use of glottal stops and unique vowel phonemes.
Sarsa is a term derived from the field of reinforcement learning, which is a subfield of artificial intelligence. It is an acronym for State-Action-Reward-State-Action, representing an algorithm used to solve Markov decision processes (MDPs). The Sarsa algorithm is particularly designed to address MDPs with discrete states and actions.
In reinforcement learning, an agent interacts with an environment, making decisions at each state based on past experiences. The objective is to determine an optimal policy that maximizes the long-term expected cumulative reward. The Sarsa algorithm performs on-policy learning, meaning it generates a policy by closely following the observed policy during training.
Within the Sarsa algorithm, the agent learns through trial and error, updating its Q-values (action-value function) based on the observed rewards and the actions taken in each state. The update follows the temporal difference learning principle, where the Q-value for a state-action pair is updated using the immediate reward obtained plus the discounted Q-value of the next state-action pair. This iterative process continues until the agent converges to an optimal policy, with Q-values accurately reflecting the expected rewards for each state-action pair.
The Sarsa algorithm strikes a balance between exploration and exploitation, allowing the agent to learn while also taking the recent policy into account. It has been widely applied in various domains, including robotics, gaming, and optimization problems. Its ability to learn through direct interaction with the environment makes it a popular choice for solving reinforcement learning tasks.
Sarsaparilla.
A practical medical dictionary. By Stedman, Thomas Lathrop. Published 1920.