InvertedDoublePendulum-v2
E1274575
UNEXPLORED
InvertedDoublePendulum-v2 is a MuJoCo-based continuous control benchmark environment in which an agent must balance and control a two-link inverted pendulum mounted on a cart.
All labels observed (1)
| Label | Occurrences |
|---|---|
| InvertedDoublePendulum-v2 canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T17521270 — 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: InvertedDoublePendulum-v2 Context triple: [MuJoCo environments, includes, InvertedDoublePendulum-v2]
-
A.
Walker2d-v2
Walker2d-v2 is a MuJoCo-based reinforcement learning benchmark task in which a simulated bipedal robot must learn to walk forward as efficiently and stably as possible.
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B.
MuJoCo physics engine
MuJoCo physics engine is a high-performance, open-source physics simulator widely used in robotics and reinforcement learning research for accurate, efficient modeling of complex dynamical systems.
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C.
MuJoCo environments
MuJoCo environments are physics-based continuous control simulation tasks widely used in reinforcement learning research and benchmarking.
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D.
Hopper-v2
Hopper-v2 is a continuous-control reinforcement learning benchmark task in MuJoCo where an agent learns to make a one-legged robot hop forward as efficiently and stably as possible.
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E.
DDPG
DDPG (Deep Deterministic Policy Gradient) is a model-free, off-policy deep reinforcement learning algorithm designed for continuous action spaces, combining ideas from DQN and actor-critic methods.
- 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: InvertedDoublePendulum-v2 Target entity description: InvertedDoublePendulum-v2 is a MuJoCo-based continuous control benchmark environment in which an agent must balance and control a two-link inverted pendulum mounted on a cart.
-
A.
Walker2d-v2
Walker2d-v2 is a MuJoCo-based reinforcement learning benchmark task in which a simulated bipedal robot must learn to walk forward as efficiently and stably as possible.
-
B.
MuJoCo physics engine
MuJoCo physics engine is a high-performance, open-source physics simulator widely used in robotics and reinforcement learning research for accurate, efficient modeling of complex dynamical systems.
-
C.
MuJoCo environments
MuJoCo environments are physics-based continuous control simulation tasks widely used in reinforcement learning research and benchmarking.
-
D.
Hopper-v2
Hopper-v2 is a continuous-control reinforcement learning benchmark task in MuJoCo where an agent learns to make a one-legged robot hop forward as efficiently and stably as possible.
-
E.
DDPG
DDPG (Deep Deterministic Policy Gradient) is a model-free, off-policy deep reinforcement learning algorithm designed for continuous action spaces, combining ideas from DQN and actor-critic methods.
- F. None of above. chosen
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.