What is recorded here?
Encapsulated parallelism and resource requirements within reusable RL components built on Ray, enabling nested rollout, optimization and model-based workloads.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Inherits Ray and implemented RL algorithms; library composition is not invention of those algorithms. Scalability/performance claims are for tested reference workloads, not all nested distributed programs.
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People and systems
Eric Liang; Richard Liaw; Robert Nishihara; Philipp Moritz; Roy Fox; Joseph Gonzalez; Ken Goldberg; Ion Stoica
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
RLlib: composable parallel reinforcement-learning components
Encapsulated parallelism and resource requirements within reusable RL components built on Ray, enabling nested rollout, optimization and model-based workloads.
Scientific scope
Inherits Ray and implemented RL algorithms; library composition is not invention of those algorithms. Scalability/performance claims are for tested reference workloads, not all nested distributed programs.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Eric Liang; Richard Liaw; Robert Nishihara; Philipp Moritz; Roy Fox; Joseph Gonzalez; Ken Goldberg; Ion Stoica
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.