Statements (70)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
open-source library
|
| gptkbp:developedBy |
gptkb:Anyscale
|
| gptkbp:documentation |
https://docs.ray.io/en/latest/rllib/index.html
|
| gptkbp:feature |
hyperparameter tuning
fault tolerance visualization tools policy evaluation cloud support policy gradient methods self-play custom callbacks custom environments model serving model-free RL multi-agent support curriculum learning imitation learning custom models custom metrics custom preprocessors multi-GPU support checkpointing policy optimization production-ready actor-critic methods model-based RL multi-node support batch RL high scalability custom policies customizable algorithms custom loss functions Q-learning methods algorithm registry asynchronous training custom exploration custom optimizers custom reward functions distributed rollout workers exploration strategies flexible APIs metrics logging multi-agent communication offline dataset ingestion parameter server architecture population-based training synchronous training |
| gptkbp:firstReleased |
2018
|
| gptkbp:focusesOn |
gptkb:reinforcement_learning
|
| https://www.w3.org/2000/01/rdf-schema#label |
Ray RLlib
|
| gptkbp:integratesWith |
gptkb:OpenAI_Gym
gptkb:TensorFlow gptkb:PyTorch gptkb:PettingZoo gptkb:RLlib_Tune |
| gptkbp:latestReleaseVersion |
Ray 2.9 (as of 2024)
|
| gptkbp:license |
gptkb:Apache_License_2.0
|
| gptkbp:partOf |
right
|
| gptkbp:programmingLanguage |
gptkb:Python
|
| gptkbp:repository |
https://github.com/ray-project/ray
|
| gptkbp:supports |
deep reinforcement learning
multi-agent reinforcement learning distributed training offline reinforcement learning |
| gptkbp:usedBy |
gptkb:researchers
industry practitioners |
| gptkbp:usedFor |
scalable RL workloads
|
| gptkbp:bfsParent |
gptkb:Proximal_Policy_Optimization_(PPO)
gptkb:Ray_(distributed_computing) |
| gptkbp:bfsLayer |
7
|