Triple
T29108627
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Neural Fitted Q-Iteration |
E736830
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | Q-learning variant |
C9067
|
CONCEPT FINISHED |
How this triple was built (1 step)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: Q-learning variant Context triple: [Neural Fitted Q-Iteration, instanceOf, Q-learning variant]
-
A.
Monte Carlo reinforcement learning algorithm
A Monte Carlo reinforcement learning algorithm is a method that learns optimal policies by estimating value functions from complete, sampled episodes of experience without requiring a model of the environment’s dynamics.
-
B.
value-based reinforcement learning method
chosen
A value-based reinforcement learning method is an approach that learns a value function estimating expected future rewards for states or state-action pairs and derives a policy by selecting actions that maximize these estimated values.
-
C.
model-based reinforcement learning algorithm
A model-based reinforcement learning algorithm is a decision-making method that learns or uses an explicit model of the environment’s dynamics to plan and select actions that maximize long-term rewards.
-
D.
reinforcement learning library
A reinforcement learning library is a software toolkit that provides algorithms, environments, and utilities to design, train, evaluate, and deploy agents that learn optimal behaviors through trial-and-error interactions with their environment.
-
E.
actor-critic method
An actor-critic method is a reinforcement learning approach that combines a policy model (actor) that selects actions with a value model (critic) that evaluates those actions to improve the policy.
- F. None of above.
Provenance (1 batch)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f077ec765c81909474c88bcc8bab43 |
completed | April 28, 2026, 9:03 a.m. |
Created at: April 28, 2026, 11:17 a.m.