[Paper Review] Ray RLLib: A Composable and Scalable Reinforcement Learning Library

Eric Liang, Richard Liaw+6 more|arXiv (Cornell University)|Dec 7, 2017

Reinforcement Learning in RoboticsComputer Science51 references109 citations

TL;DR

Ray RLLib introduces a composable and scalable reinforcement learning library built on Ray’s task-based programming model, enabling fine-grained parallelism and modular reuse of RL components. By encapsulating resource and computation needs within individual components, it achieves performance on par with or better than highly optimized reference implementations while supporting a wide range of state-of-the-art algorithms.

ABSTRACT

Reinforcement learning (RL) algorithms involve the deep nesting of distinct components, where each component typically exhibits opportunities for distributed computation. Current RL libraries offer parallelism at the level of the entire program, coupling all the components together and making existing implementations difficult to extend, combine, and reuse. We argue for building composable RL components by encapsulating parallelism and resource requirements within individual components, which can be achieved by building on top of a flexible task-based programming model. We demonstrate this principle by building Ray RLLib on top of Ray and show that we can implement a wide range of state-of-the-art algorithms by composing and reusing a handful of standard components. This composability does not come at the cost of performance --- in our experiments, RLLib matches or exceeds the performance of highly optimized reference implementations. Ray RLLib is available as part of Ray at this https URL

Motivation & Objective

• To address the rigidity of existing RL libraries that couple components and limit extensibility.
• To enable fine-grained, composable parallelism by encapsulating resource and computation requirements within individual RL components.
• To demonstrate that complex RL algorithms can be implemented by composing a small set of reusable, scalable components.
• To achieve high performance without sacrificing composability or extensibility.
• To provide a flexible, extensible framework for implementing and experimenting with modern RL algorithms.

Proposed method

• Building Ray RLLib on top of Ray’s task-based programming model to enable dynamic, fine-grained task scheduling and resource management.
• Encapsulating each RL component—such as policy, value function, and replay buffer—within isolated, composable units with defined resource and computation requirements.
• Using Ray’s distributed execution model to enable transparent distribution of components across multiple machines and GPUs.
• Designing components to be composable via a modular interface, allowing arbitrary composition of standard RL building blocks.
• Leveraging Ray’s fault tolerance and automatic scaling to handle dynamic workloads and resource allocation.
• Implementing standard RL algorithms by composing these reusable components without low-level reimplementation.

Experimental results

Research questions

• RQ1 Can RL components be made composable while maintaining high performance and scalability?
• RQ2 Does encapsulating parallelism and resource management within individual components improve extensibility and reuse?
• RQ3 Can a wide range of state-of-the-art RL algorithms be implemented using only a small set of standardized, composable components?
• RQ4 Does the composable architecture of Ray RLLib match or exceed the performance of highly optimized, monolithic RL implementations?
• RQ5 How does the task-based programming model in Ray enable efficient and flexible distribution of RL components?

Key findings

• Ray RLLib successfully implements a broad range of state-of-the-art RL algorithms using only a small set of composable, reusable components.
• The composable architecture enables seamless reuse and combination of components without sacrificing performance.
• Ray RLLib matches or exceeds the performance of highly optimized reference implementations in benchmark environments.
• The task-based model in Ray enables efficient, transparent distribution of RL components across heterogeneous hardware.
• Encapsulating resource and computation requirements within components improves modularity and simplifies extension and experimentation.
• The system demonstrates that high performance and composability are not mutually exclusive in RL library design.