RLlib
E95190
RLlib is a scalable, open-source reinforcement learning library built on Ray that provides high-level APIs and distributed training support for a wide range of RL algorithms.
All labels observed (1)
| Label | Occurrences |
|---|---|
| RLlib canonical | 4 |
How this entity was disambiguated
This entity first appeared as the object of triple T805157 — 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: RLlib Context triple: [OpenAI Gym, influenced, RLlib]
-
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.
OpenAI Gym
OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms through a standardized collection of environments and interfaces.
-
C.
TensorFlow
TensorFlow is an open-source, end-to-end machine learning and deep learning framework widely used for building, training, and deploying neural network models at scale.
-
D.
MuZero
MuZero is a DeepMind reinforcement learning algorithm that learns to plan and master complex games like Go, chess, and Atari without being given the rules in advance.
-
E.
Element AI
Element AI was a Montreal-based artificial intelligence company and research lab known for developing enterprise AI solutions and advancing deep learning research.
- 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: RLlib Target entity description: RLlib is a scalable, open-source reinforcement learning library built on Ray that provides high-level APIs and distributed training support for a wide range of RL algorithms.
-
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.
OpenAI Gym
OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms through a standardized collection of environments and interfaces.
-
C.
TensorFlow
TensorFlow is an open-source, end-to-end machine learning and deep learning framework widely used for building, training, and deploying neural network models at scale.
-
D.
MuZero
MuZero is a DeepMind reinforcement learning algorithm that learns to plan and master complex games like Go, chess, and Atari without being given the rules in advance.
-
E.
Element AI
Element AI was a Montreal-based artificial intelligence company and research lab known for developing enterprise AI solutions and advancing deep learning research.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
machine learning framework
ⓘ
open-source software ⓘ reinforcement learning library ⓘ |
| designedFor |
production workloads
ⓘ
research workloads ⓘ scalability ⓘ |
| developedOnTopOf | Ray ⓘ |
| hostedOn | GitHub ⓘ |
| integratesWith |
Ray Serve
ⓘ
Ray Tune ⓘ |
| license | Apache License 2.0 ⓘ |
| partOf | Ray ecosystem ⓘ |
| provides |
algorithm configuration system
ⓘ
built-in RL algorithms ⓘ checkpointing utilities ⓘ custom model support ⓘ custom policy support ⓘ evaluation utilities ⓘ high-level APIs ⓘ hyperparameter tuning integration ⓘ logging utilities ⓘ low-level APIs ⓘ |
| supports |
CPU training
ⓘ
GPU training ⓘ distributed reinforcement learning ⓘ multi-GPU training ⓘ multi-node training ⓘ scalable training ⓘ |
| supportsAlgorithmFamily |
Q-learning methods
ⓘ
actor-critic methods ⓘ evolution strategies ⓘ multi-agent reinforcement learning ⓘ policy gradient methods ⓘ |
| supportsEnvironmentInterface |
Gymnasium
ⓘ
OpenAI Gym ⓘ PettingZoo ⓘ |
| supportsFeature |
centralized training with decentralized execution
ⓘ
distributed rollout workers ⓘ fault-tolerant training ⓘ parameter server architectures ⓘ |
| supportsFramework |
PyTorch
ⓘ
TensorFlow ⓘ |
| supportsUseCase |
hierarchical reinforcement learning
ⓘ
model-based reinforcement learning ⓘ multi-agent reinforcement learning ⓘ offline reinforcement learning ⓘ self-play ⓘ single-agent reinforcement learning ⓘ |
| 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: RLlib Description of subject: RLlib is a scalable, open-source reinforcement learning library built on Ray that provides high-level APIs and distributed training support for a wide range of RL algorithms.
Referenced by (4)
Full triples — surface form annotated when it differs from this entity's canonical label.