SARSA
E1335314
UNEXPLORED
SARSA is an on-policy reinforcement learning algorithm that updates action values based on the current state–action pair, the reward, and the next state–action pair.
All labels observed (2)
How this entity was disambiguated
This entity first appeared as the object of triple T18629563 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SARSA Context triple: [Q-learning, isRelatedTo, SARSA]
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A.
Q-learning
Q-learning is a model-free reinforcement learning algorithm that learns an action-value function to optimize decision-making by estimating the expected cumulative reward for each state-action pair.
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B.
REINFORCE
REINFORCE is a classic Monte Carlo policy gradient algorithm in reinforcement learning that optimizes stochastic policies by estimating gradients from sampled returns.
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C.
SAC
The SAC is the abbreviated name commonly used for the State Affairs Commission, the top governing body in North Korea responsible for major state policy and leadership.
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D.
SAC
SAC is a NATO-led multinational program that provides participating nations with shared strategic airlift capabilities using a fleet of C-17 Globemaster III aircraft.
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E.
SAC
SAC is the company that manages Catania–Fontanarossa Airport, one of the main air transport hubs in Sicily, Italy.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: SARSA Target entity description: SARSA is an on-policy reinforcement learning algorithm that updates action values based on the current state–action pair, the reward, and the next state–action pair.
-
A.
Q-learning
Q-learning is a model-free reinforcement learning algorithm that learns an action-value function to optimize decision-making by estimating the expected cumulative reward for each state-action pair.
-
B.
REINFORCE
REINFORCE is a classic Monte Carlo policy gradient algorithm in reinforcement learning that optimizes stochastic policies by estimating gradients from sampled returns.
-
C.
SAC
The SAC is the abbreviated name commonly used for the State Affairs Commission, the top governing body in North Korea responsible for major state policy and leadership.
-
D.
SAC
SAC is a NATO-led multinational program that provides participating nations with shared strategic airlift capabilities using a fleet of C-17 Globemaster III aircraft.
-
E.
SAC
SAC is the company that manages Catania–Fontanarossa Airport, one of the main air transport hubs in Sicily, Italy.
- F. None of above. chosen
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.