MuJoCo environments
E426682
continuous control task collection
physics-based simulation environment
reinforcement learning benchmark suite
MuJoCo environments are physics-based continuous control simulation tasks widely used in reinforcement learning research and benchmarking.
All labels observed (1)
| Label | Occurrences |
|---|---|
| MuJoCo environments canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4277531 — 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: MuJoCo environments Context triple: [TF-Agents, supportsEnvironment, MuJoCo environments]
-
A.
OpenAI Gym
OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms through a standardized collection of environments and interfaces.
-
B.
OpenAI Baselines
OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms released by OpenAI for research and benchmarking.
-
C.
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.
-
D.
TF-Agents
TF-Agents is an open-source library built on TensorFlow that provides modular components and tools for developing, training, and evaluating reinforcement learning algorithms.
-
E.
Stable Baselines
Stable Baselines is a popular Python library that provides reliable, well-tested implementations of reinforcement learning algorithms built on top of OpenAI Baselines.
- 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: MuJoCo environments Target entity description: MuJoCo environments are physics-based continuous control simulation tasks widely used in reinforcement learning research and benchmarking.
-
A.
OpenAI Gym
OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms through a standardized collection of environments and interfaces.
-
B.
OpenAI Baselines
OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms released by OpenAI for research and benchmarking.
-
C.
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.
-
D.
TF-Agents
TF-Agents is an open-source library built on TensorFlow that provides modular components and tools for developing, training, and evaluating reinforcement learning algorithms.
-
E.
Stable Baselines
Stable Baselines is a popular Python library that provides reliable, well-tested implementations of reinforcement learning algorithms built on top of OpenAI Baselines.
- F. None of above. chosen
Statements (56)
| Predicate | Object |
|---|---|
| instanceOf |
continuous control task collection
ⓘ
physics-based simulation environment ⓘ reinforcement learning benchmark suite ⓘ |
| actionControls |
joint positions
ⓘ
joint torques ⓘ joint velocities ⓘ |
| basedOn | MuJoCo physics engine NERFINISHED ⓘ |
| benchmarkFor |
actor-critic algorithms
ⓘ
exploration algorithms ⓘ model-based reinforcement learning ⓘ offline reinforcement learning ⓘ policy gradient methods ⓘ |
| commonlyAccessedVia |
Gymnasium
NERFINISHED
ⓘ
OpenAI Gym NERFINISHED ⓘ dm_control NERFINISHED ⓘ |
| domain |
locomotion
ⓘ
manipulation ⓘ robotics ⓘ |
| evaluationMetric | average episodic return ⓘ |
| hasActionSpaceType | continuous ⓘ |
| hasObservationSpaceType | continuous ⓘ |
| hasProperty | differentiable physics engine (MuJoCo core) ⓘ |
| includes |
Ant-v2
NERFINISHED
ⓘ
HalfCheetah-v2 NERFINISHED ⓘ Hopper-v2 NERFINISHED ⓘ Humanoid-v2 NERFINISHED ⓘ InvertedDoublePendulum-v2 NERFINISHED ⓘ InvertedPendulum-v2 NERFINISHED ⓘ Pusher-v2 NERFINISHED ⓘ Reacher-v2 NERFINISHED ⓘ Striker-v2 NERFINISHED ⓘ Swimmer-v2 NERFINISHED ⓘ Thrower-v2 NERFINISHED ⓘ Walker2d-v2 NERFINISHED ⓘ |
| requires | MuJoCo license (historically) ⓘ |
| simulationType | rigid-body dynamics ⓘ |
| stateIncludes |
body orientations
ⓘ
contact information ⓘ joint positions ⓘ joint velocities ⓘ |
| supports |
actuated joints
ⓘ
contact dynamics ⓘ deterministic dynamics (given seed) ⓘ joint constraints ⓘ multi-body systems ⓘ stochastic policies ⓘ |
| timeStep | fixed simulation timestep ⓘ |
| typicalRewardStructure |
dense reward
ⓘ
task-specific reward ⓘ |
| typicalUseCase | comparing reinforcement learning algorithms under standardized tasks ⓘ |
| usedFor |
algorithm benchmarking
ⓘ
continuous control evaluation ⓘ policy optimization experiments ⓘ reinforcement learning research ⓘ |
| widelyUsedIn |
DeepMind control suite experiments
NERFINISHED
ⓘ
continuous control benchmarks such as OpenAI Baselines ⓘ |
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: MuJoCo environments Description of subject: MuJoCo environments are physics-based continuous control simulation tasks widely used in reinforcement learning research and benchmarking.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.