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.

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All labels observed (5)

Label	Occurrences
DeepMind control suite experiments	1
HalfCheetah-v2	1
InvertedPendulum-v2	1
MuJoCo benchmarks	1
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 ⓘ
benchmarkFor	actor-critic algorithms ⓘ exploration algorithms ⓘ model-based reinforcement learning ⓘ offline reinforcement learning ⓘ policy gradient methods ⓘ
commonlyAccessedVia	Gymnasium ⓘ OpenAI Gym ⓘ dm_control ⓘ
domain	locomotion ⓘ manipulation ⓘ robotics ⓘ
evaluationMetric	average episodic return ⓘ
hasActionSpaceType	continuous ⓘ
hasObservationSpaceType	continuous ⓘ
hasProperty	differentiable physics engine (MuJoCo core) ⓘ
includes	Ant-v2 ⓘ HalfCheetah-v2 ⓘ linked to: MuJoCo environments Hopper-v2 ⓘ Humanoid-v2 ⓘ linked to: OpenAI Gym InvertedDoublePendulum-v2 ⓘ InvertedPendulum-v2 ⓘ linked to: MuJoCo environments Pusher-v2 ⓘ Reacher-v2 ⓘ Striker-v2 ⓘ Swimmer-v2 ⓘ Thrower-v2 ⓘ Walker2d-v2 ⓘ
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 ⓘ linked to: MuJoCo environments continuous control benchmarks such as OpenAI Baselines ⓘ

How these facts were elicited

Referenced by (5)

Full triples — surface form annotated when it differs from this entity's canonical label.

TF-Agents → supportsEnvironment → MuJoCo environments ⓘ

Soft Actor-Critic → commonlyEvaluatedOn → MuJoCo benchmarks ⓘ

subject linked to: SAC

linked to: MuJoCo environments

MuJoCo environments → includes → HalfCheetah-v2 ⓘ

linked to: MuJoCo environments

MuJoCo environments → includes → InvertedPendulum-v2 ⓘ

linked to: MuJoCo environments

MuJoCo environments → widelyUsedIn → DeepMind control suite experiments ⓘ

linked to: MuJoCo environments

All labels observed (5)

How this entity was disambiguated Show

Statements (56)

How these facts were elicited Show

Referenced by (5)

How this entity was disambiguated

How these facts were elicited