Stable Baselines
E99657
Stable Baselines is a popular Python library that provides reliable, well-tested implementations of reinforcement learning algorithms built on top of OpenAI Baselines.
All labels observed (6)
| Label | Occurrences |
|---|---|
| Stable Baselines canonical | 5 |
| Stable Baselines3 | 3 |
| Stable-Baselines | 1 |
| Stable-Baselines3 | 1 |
| Stable-Baselines3 (as PPO successor, conceptually similar) | 1 |
| Stable-Baselines3 (via wrappers) | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T824114 — 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: Stable Baselines Context triple: [OpenAI Baselines, relatedTo, Stable Baselines]
-
A.
OpenAI Baselines
OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms released by OpenAI for research and benchmarking.
-
B.
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.
-
C.
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.
-
D.
OpenAI Gym
OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms through a standardized collection of environments and interfaces.
-
E.
Prioritized Experience Replay DQN
Prioritized Experience Replay DQN is a variant of the Deep Q-Network algorithm that improves learning efficiency by sampling more informative experiences with higher priority from the replay buffer.
- 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: Stable Baselines Target entity description: Stable Baselines is a popular Python library that provides reliable, well-tested implementations of reinforcement learning algorithms built on top of OpenAI Baselines.
-
A.
OpenAI Baselines
OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms released by OpenAI for research and benchmarking.
-
B.
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.
-
C.
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.
-
D.
OpenAI Gym
OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms through a standardized collection of environments and interfaces.
-
E.
Prioritized Experience Replay DQN
Prioritized Experience Replay DQN is a variant of the Deep Q-Network algorithm that improves learning efficiency by sampling more informative experiences with higher priority from the replay buffer.
- F. None of above. chosen
Statements (50)
| Predicate | Object |
|---|---|
| instanceOf |
Python library
ⓘ
reinforcement learning library ⓘ software library ⓘ |
| basedOn | OpenAI Baselines ⓘ |
| category |
machine learning software
ⓘ
open-source software ⓘ |
| compatibleWith |
NumPy
ⓘ
TensorFlow (original versions) ⓘ |
| developedIn | Python ecosystem ⓘ |
| focusesOn |
ease of use for RL practitioners
ⓘ
reproducible reinforcement learning research ⓘ |
| goal |
provide reliable RL algorithm implementations
ⓘ
standardize RL research codebases ⓘ |
| hasDocumentation | online documentation website ⓘ |
| hasFeature |
logging utilities
ⓘ
model saving and loading ⓘ tensorboard integration ⓘ unified interface for RL algorithms ⓘ vectorized environments ⓘ |
| hasSuccessor |
Stable Baselines
self-linksurface differs
ⓘ
surface form:
Stable Baselines3
|
| hostedOn | GitHub ⓘ |
| implements | reinforcement learning algorithms ⓘ |
| license | MIT License ⓘ |
| programmingLanguage | Python ⓘ |
| provides |
reliable implementations of RL algorithms
ⓘ
well-tested implementations of RL algorithms ⓘ |
| relatedTo |
OpenAI Baselines
ⓘ
Stable Baselines self-linksurface differs ⓘ
surface form:
Stable Baselines3
|
| supports |
actor-critic methods
ⓘ
continuous action spaces ⓘ discrete action spaces ⓘ parallel environment execution ⓘ policy gradient methods ⓘ value-based methods ⓘ |
| supportsAlgorithm |
A2C
ⓘ
ACKTR ⓘ DDPG ⓘ DQN ⓘ PPO ⓘ SAC ⓘ TD3 ⓘ |
| supportsEnvironmentInterface |
Gymnasium
ⓘ
OpenAI Gym ⓘ |
| targetUser |
data scientists
ⓘ
machine learning researchers ⓘ reinforcement learning practitioners ⓘ |
| usedFor |
applied reinforcement learning projects
ⓘ
benchmarking RL algorithms ⓘ training reinforcement learning agents ⓘ |
| writtenIn | Python ⓘ |
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: Stable Baselines Description of subject: Stable Baselines is a popular Python library that provides reliable, well-tested implementations of reinforcement learning algorithms built on top of OpenAI Baselines.
Referenced by (12)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
Stable Baselines3
this entity surface form:
Stable Baselines3
this entity surface form:
Stable-Baselines3 (via wrappers)
this entity surface form:
Stable Baselines3
this entity surface form:
Stable-Baselines3
this entity surface form:
Stable-Baselines
this entity surface form:
Stable-Baselines3 (as PPO successor, conceptually similar)