VectorEnv interface
E438353
The VectorEnv interface is a Gymnasium API for running multiple reinforcement learning environments in parallel as a single batched environment to enable more efficient data collection and training.
All labels observed (1)
| Label | Occurrences |
|---|---|
| VectorEnv interface canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4425285 — 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: VectorEnv interface Context triple: [Gymnasium, hasAPI, VectorEnv interface]
-
A.
MuJoCo environments
MuJoCo environments are physics-based continuous control simulation tasks widely used in reinforcement learning research and benchmarking.
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B.
OpenAI Gym
OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms through a standardized collection of environments and interfaces.
-
C.
Virtual Execution System
The Virtual Execution System is the runtime environment of the Common Language Infrastructure that loads, manages, and executes compiled code in a platform-agnostic way.
-
D.
Arcade Learning Environment
Arcade Learning Environment is a widely used research platform that provides a suite of Atari 2600 games for developing and evaluating reinforcement learning algorithms.
-
E.
AI2-THOR
AI2-THOR is an interactive 3D simulation platform designed for training and evaluating embodied AI agents in visually rich, physics-enabled environments.
- 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: VectorEnv interface Target entity description: The VectorEnv interface is a Gymnasium API for running multiple reinforcement learning environments in parallel as a single batched environment to enable more efficient data collection and training.
-
A.
MuJoCo environments
MuJoCo environments are physics-based continuous control simulation tasks widely used in reinforcement learning research and benchmarking.
-
B.
OpenAI Gym
OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms through a standardized collection of environments and interfaces.
-
C.
Virtual Execution System
The Virtual Execution System is the runtime environment of the Common Language Infrastructure that loads, manages, and executes compiled code in a platform-agnostic way.
-
D.
Arcade Learning Environment
Arcade Learning Environment is a widely used research platform that provides a suite of Atari 2600 games for developing and evaluating reinforcement learning algorithms.
-
E.
AI2-THOR
AI2-THOR is an interactive 3D simulation platform designed for training and evaluating embodied AI agents in visually rich, physics-enabled environments.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
Gymnasium API component
ⓘ
software interface ⓘ |
| canBeBackedBy |
in-place batched environments without OS-level parallelism
ⓘ
process-based parallelism ⓘ thread-based parallelism ⓘ |
| compatibleWith | Gymnasium Env API NERFINISHED ⓘ |
| defines | common API for vectorized environments ⓘ |
| designGoal |
abstract away details of parallel environment management
ⓘ
provide uniform interface for different vectorization backends ⓘ |
| documentationURL | https://gymnasium.farama.org/api/vector/ ⓘ |
| domain | reinforcement learning ⓘ |
| enables |
higher throughput environment interaction
ⓘ
more stable gradient estimates via larger batch sizes ⓘ parallel rollout collection ⓘ |
| hasMethod |
call
ⓘ
close ⓘ get_attr ⓘ reset ⓘ seed ⓘ set_attr ⓘ step ⓘ |
| hasProperty |
action_space
ⓘ
num_envs ⓘ observation_space ⓘ single_action_space ⓘ single_observation_space ⓘ |
| implementedBy |
AsyncVectorEnv
NERFINISHED
ⓘ
SyncVectorEnv NERFINISHED ⓘ other custom vectorized environment classes ⓘ |
| input | batched actions for all sub-environments ⓘ |
| output |
batched observations from all sub-environments
ⓘ
batched rewards from all sub-environments ⓘ batched terminated flags from all sub-environments ⓘ batched truncated flags from all sub-environments ⓘ |
| partOf | Gymnasium ⓘ |
| relatedTo | single-environment Env interface in Gymnasium ⓘ |
| successorOf | vectorized environment patterns used in OpenAI Gym ⓘ |
| supports |
batched actions
ⓘ
batched observations ⓘ batched rewards ⓘ batched termination signals ⓘ synchronous stepping of multiple environments ⓘ |
| targetUser |
reinforcement learning practitioners
ⓘ
reinforcement learning researchers ⓘ |
| usedFor |
batched environment execution
ⓘ
efficient data collection for reinforcement learning ⓘ parallel simulation for training agents ⓘ running multiple reinforcement learning environments in parallel ⓘ |
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: VectorEnv interface Description of subject: The VectorEnv interface is a Gymnasium API for running multiple reinforcement learning environments in parallel as a single batched environment to enable more efficient data collection and training.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.