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RLlib: Abstractions for Distributed Reinforcement Learning

arXiv 1712.09381 ↗First posted 26 Dec 2017cs.AI cs.DC cs.LGcited by 53

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Abstract (arXiv)

Reinforcement learning (RL) algorithms involve the deep nesting of highly irregular computation patterns, each of which typically exhibits opportunities for distributed computation. We argue for distributing RL components in a composable way by adapting algorithms for top-down hierarchical control, thereby encapsulating parallelism and resource requirements within short-running compute tasks. We demonstrate the benefits of this principle through RLlib: a library that provides scalable software primitives for RL. These primitives enable a broad range of algorithms to be implemented with high performance, scalability, and substantial code reuse. RLlib is available at https://rllib.io/.

Key facts. Cited by 53 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Dec 2017.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 0; 2021: 16; 2022: 14; 2023: 13; 2024: 7; 2025: 1; 2026: 1.

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2023RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmarkcited by 70
2024DistRL: An Asynchronous Distributed Reinforcement Learning Framework for On-Device Control Agentscited by 68
2021Generalization in Mean Field Games by Learning Master Policiescited by 28
2022Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2021cited by 23
2021The Neural MMO Platform for Massively Multiagent Researchcited by 17
2021NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environmentcited by 15
2022gym-DSSAT: a crop model turned into a Reinforcement Learning environmentcited by 13
2021Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximizationcited by 11
2024A Review of Reinforcement Learning in Financial Applicationscited by 11
2021Target Entropy Annealing for Discrete Soft Actor-Criticcited by 10
2023SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Corescited by 10
2022Consistent Dropout for Policy Gradient Reinforcement Learningcited by 9

38 more

2018LIFT: Reinforcement Learning in Computer Systems by Learning From Demonstrationscited by 8
2024Acceleration for Deep Reinforcement Learning using Parallel and Distributed Computing: A Surveycited by 8
2022POGEMA: Partially Observable Grid Environment for Multiple Agentscited by 6
2024Goal-conditioned reinforcement learning for ultrasound navigation guidancecited by 6
2021Machine Learning aided Crop Yield Optimizationcited by 5
2025Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid Topology Controlcited by 5
2021MarsExplorer: Exploration of Unknown Terrains via Deep Reinforcement Learning and Procedurally Generated Environmentscited by 4
2022Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithmscited by 4
2023Deep Multi-Agent Reinforcement Learning for Decentralized Active Hypothesis Testingcited by 4
2021Improved Cooperation by Exploiting a Common Signalcited by 3
2021AI-driven Prices for Externalities and Sustainability in Production Marketscited by 3
2021Cogment: Open Source Framework For Distributed Multi-actor Training, Deployment & Operationscited by 3
2023Creating Valid Adversarial Examples of Malwarecited by 3
2023Modeling Boundedly Rational Agents with Latent Inference Budgetscited by 3
2021Explanation-Aware Experience Replay in Rule-Dense Environmentscited by 2
2021Parallel Actors and Learners: A Framework for Generating Scalable RL Implementationscited by 2
2021Decentralized Cooperative Lane Changing at Freeway Weaving Areas Using Multi-Agent Deep Reinforcement Learningcited by 2
2022Outcome-Guided Counterfactuals for Reinforcement Learning Agents from a Jointly Trained Generative Latent Spacecited by 2
2023Graph Attention Multi-Agent Fleet Autonomy for Advanced Air Mobilitycited by 2
2023Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Controlcited by 2
2024Interpretable and Efficient Data-driven Discovery and Control of Distributed Systemscited by 2
2021Resource Allocation in Disaggregated Data Centre Systems with Reinforcement Learningcited by 1
2021Entropy-Aware Model Initialization for Effective Exploration in Deep Reinforcement Learningcited by 1
2022Project proposal: A modular reinforcement learning based automated theorem provercited by 1
2023Exposure-Based Multi-Agent Inspection of a Tumbling Target Using Deep Reinforcement Learningcited by 1
2023Deep Reinforcement Learning for the Joint Control of Traffic Light Signaling and Vehicle Speed Advicecited by 1
2024Towards Fault Tolerance in Multi-Agent Reinforcement Learningcited by 1
2021Two Approaches to Building Collaborative, Task-Oriented Dialog Agents through Self-Playcited by 0
2022Neural-Progressive Hedging: Enforcing Constraints in Reinforcement Learning with Stochastic Programmingcited by 0
2022JORLDY: a fully customizable open source framework for reinforcement learningcited by 0
2022Many Field Packet Classification with Decomposition and Reinforcement Learningcited by 0
2022High Performance Simulation for Scalable Multi-Agent Reinforcement Learningcited by 0
2022Hierarchical Decentralized Deep Reinforcement Learning Architecture for a Simulated Four-Legged Agentcited by 0
2022One-shot, Offline and Production-Scalable PID Optimisation with Deep Reinforcement Learningcited by 0
2022Network Aware Compute and Memory Allocation in Optically Composable Data Centres with Deep Reinforcement Learning and Graph Neural Networkscited by 0
2023Plume: A Framework for High Performance Deep RL Network Controllers via Prioritized Trace Samplingcited by 0
2023Parameter Optimization of LLC-Converter with multiple operation points using Reinforcement Learningcited by 0
2023On the Benefits of Biophysical Synapsescited by 0

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