Universal Value Function Approximators
E441116
Universal Value Function Approximators (UVFA) are a reinforcement learning framework that generalizes value functions over both states and goals, enabling agents to learn goal-conditioned behaviors in a unified way.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Universal Value Function Approximators canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4470554 — 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: Universal Value Function Approximators Context triple: [Hindsight Experience Replay, relatedTo, Universal Value Function Approximators]
-
A.
Generalized Advantage Estimation
Generalized Advantage Estimation is a reinforcement learning technique that reduces variance and improves sample efficiency in policy gradient methods by cleverly estimating the advantage function over multiple time scales.
-
B.
Proximal Policy Optimization
Proximal Policy Optimization is a popular reinforcement learning algorithm that improves policy gradient methods by using clipped objective functions to achieve stable and efficient training.
-
C.
Asynchronous Advantage Actor-Critic
Asynchronous Advantage Actor-Critic is a deep reinforcement learning algorithm that trains multiple parallel agents to learn both policy and value functions efficiently and stably.
-
D.
Atari deep Q-network
The Atari deep Q-network is a pioneering deep reinforcement learning system that learned to play a wide range of Atari 2600 video games directly from raw pixels at human-level or better performance.
-
E.
Asynchronous Methods for Deep Reinforcement Learning
"Asynchronous Methods for Deep Reinforcement Learning" is a 2016 DeepMind paper that introduced asynchronous parallel training techniques for deep reinforcement learning, most notably the A3C algorithm, enabling more stable and efficient learning without specialized hardware.
- 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: Universal Value Function Approximators Target entity description: Universal Value Function Approximators (UVFA) are a reinforcement learning framework that generalizes value functions over both states and goals, enabling agents to learn goal-conditioned behaviors in a unified way.
-
A.
Generalized Advantage Estimation
Generalized Advantage Estimation is a reinforcement learning technique that reduces variance and improves sample efficiency in policy gradient methods by cleverly estimating the advantage function over multiple time scales.
-
B.
Proximal Policy Optimization
Proximal Policy Optimization is a popular reinforcement learning algorithm that improves policy gradient methods by using clipped objective functions to achieve stable and efficient training.
-
C.
Asynchronous Advantage Actor-Critic
Asynchronous Advantage Actor-Critic is a deep reinforcement learning algorithm that trains multiple parallel agents to learn both policy and value functions efficiently and stably.
-
D.
Atari deep Q-network
The Atari deep Q-network is a pioneering deep reinforcement learning system that learned to play a wide range of Atari 2600 video games directly from raw pixels at human-level or better performance.
-
E.
Asynchronous Methods for Deep Reinforcement Learning
"Asynchronous Methods for Deep Reinforcement Learning" is a 2016 DeepMind paper that introduced asynchronous parallel training techniques for deep reinforcement learning, most notably the A3C algorithm, enabling more stable and efficient learning without specialized hardware.
- F. None of above. chosen
Statements (46)
| Predicate | Object |
|---|---|
| instanceOf |
goal-conditioned value function model
ⓘ
reinforcement learning framework ⓘ |
| abbreviation | UVFA ⓘ |
| addresses | lack of generalization across goals in standard value functions ⓘ |
| approximatorType | parametric function approximator ⓘ |
| assumes | shared structure across goals ⓘ |
| citationVenue | Proceedings of the 32nd International Conference on Machine Learning NERFINISHED ⓘ |
| commonImplementation | neural network ⓘ |
| compatibleWith |
Q-learning
NERFINISHED
ⓘ
actor-critic methods ⓘ policy gradient methods ⓘ |
| coreIdea |
generalize value functions over both states and goals
ⓘ
represent value as a function of state and goal ⓘ |
| enables |
generalization to unseen goals
ⓘ
goal-conditioned policies ⓘ multi-goal reinforcement learning ⓘ transfer across goals ⓘ |
| evaluationDomain |
grid-world tasks
ⓘ
navigation tasks ⓘ |
| evaluationMetric | performance on multiple goals ⓘ |
| field |
machine learning
ⓘ
reinforcement learning ⓘ |
| formalization | V(s,g) as value function over state s and goal g ⓘ |
| goalRepresentation |
can be continuous
ⓘ
can be discrete ⓘ |
| inputIncludes |
goal representation
ⓘ
state representation ⓘ |
| inspired | universal function approximation in supervised learning ⓘ |
| introducedBy |
Daniel Horgan
NERFINISHED
ⓘ
David Silver NERFINISHED ⓘ Karol Gregor NERFINISHED ⓘ Tom Schaul NERFINISHED ⓘ |
| learningSignal | temporal-difference error ⓘ |
| organization | DeepMind NERFINISHED ⓘ |
| outputRepresents | expected return for given state and goal ⓘ |
| publicationTitle | Universal Value Function Approximators NERFINISHED ⓘ |
| publicationYear | 2015 ⓘ |
| publishedIn |
ICML 2015
NERFINISHED
ⓘ
International Conference on Machine Learning NERFINISHED ⓘ |
| relatedTo |
goal-conditioned reinforcement learning
ⓘ
hindsight experience replay ⓘ successor features ⓘ universal policy approximators ⓘ |
| usedFor |
generalization over goal space
ⓘ
multi-task learning in reinforcement learning ⓘ transfer learning in reinforcement learning ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Universal Value Function Approximators Description of subject: Universal Value Function Approximators (UVFA) are a reinforcement learning framework that generalizes value functions over both states and goals, enabling agents to learn goal-conditioned behaviors in a unified way.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.