GPU-Accelerated RL

RL: Reinforcement Learning

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15 papers5 papers in the last four weeks, up 67% on the four weeks before. 0.0% of all new papers.

Reinforcement learning for hardware acceleration and system-level optimization on FPGA and integrated-circuit platforms: A survey
Oct 20, 2026Hamza KheddarGPU-Accelerated RL Reinforcement Learning
LSEA Laboratory, Department of Electrical Engineering, University of Medea, 26000, Algeria
JAXtari: High-Throughput and Easy-to-Modify Arcade Learning Environment
Oct 16, 2026Quentin Delfosse, Raban Emunds, Paul Seitz +5GPU-Accelerated RL RL Benchmarks
Department of Computer Science, Technical University Darmstadt, Germany · Google Intrinsic AI Research · Hessian Center for Artificial Intelligence (hessian.AI) +2
Massively Parallel Reinforcement Learning with a Chaotic Reconfigurable Clockless Chip
Sep 28, 2026Eric Oliveira-Gomes, Damien RontaniReinforcement Learning GPU-Accelerated RL
CentraleSup´elec and Universit´e de Lorraine, LMOPS UR4423 Laboratory, Metz F-57070, France
PEARL: Adaptive Prefill-Decode Execution with Elasticity for Agentic Reinforcement Learning
Sep 28, 2026Jiaan Zhu, Wei Gao, Youhui Bai +6Agentic RL Asynchronous RL
USTC China
Nereus: Adaptive Parallelism for LLM Post-Training
Sep 28, 2026Songlin Jiang, Tuo Shi, Sitong Zhang +3LLM Inference Scheduling RL Post-Training
Aalto University · Shenzhen University of Advanced Technology · Zhejiang University
QwenGyre: An Elastic Reinforcement Learning Framework for Training xLong-Horizon Agents
Sep 27, 2026Weiqi Wang, Yuxin Zhou, Mouxiang Chen +9Reinforcement Learning Long-Horizon Agent Tasks
Alibaba Token Hub, Alibaba Group · University of Science and Technology of China · Tsinghua University
HiLoRe: What to Store, Compress, or Recompute for Efficient GRPO Training
Sep 27, 2026Xinrui Chen, Mengyang Li, Ou Wu +1Group Relative Policy Optimization Activation Compression
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China · Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin, China · University of Southern Queensland
InstantMimic: A High Performance System for Learning Physics-based Skills in Seconds
Sep 9, 2026Ikjun Choi, Geonho Leem, Jungdam WonReinforcement Learning Imitation Learning
Seoul National University, Seoul, South Korea
rl-triton: High-Performance Triton GPU Kernels for Reinforcement Learning Credit Assignment
Aug 18, 2026Lars Simon ZehnderCredit Assignment in RL Reinforcement Learning
Independent Researcher Faro, Portugal
CRAX: Fast Safe Reinforcement Learning Benchmarking
Jun 18, 2026Tristan Tomilin, Mourad Boustani, Mickey Beurskens +2RL Benchmarks Reinforcement Learning
Eindhoven University of Technology
FlashNav: Training Deployable Robot Navigation Policies in Seconds
Jun 14, 2026Shanze Wang, Yiwei Qian, Xinming Zhang +8Robot Policy Learning Reinforcement Learning
Eastern Institute of Technology, Ningbo · The Hong Kong Polytechnic University · National University of Singapore +2
TensorHub: Scalable and Elastic Weight Transfer for LLM RL Training
Apr 10, 2026Chenhao Ye, Huaizheng Zhang, Mingcong Han +11RL for Language Models Communication-Efficient Distributed Training
ByteDance Seed · University of Wisconsin–Madison
MO-Playground: Massively Parallelized Multi-Objective Reinforcement Learning for Robotics
Mar 10, 2026Neil Janwani, Ellen Novoseller, Vernon J. Lawhern +1GPU-Accelerated RL Multi-Objective Reinforcement Learning
Georgia Institute of Technology, Atlanta, GA USA · DEVCOM Army Research Laboratory, MD, USA
Octax: Accelerated CHIP-8 Arcade Environments for Reinforcement Learning in JAX
Oct 2, 2025Waris Radji, Thomas Michel, Hector PiteauRL Benchmarks Reinforcement Learning
Univ. Lille, Inria, CNRS, Centrale Lille, UMR 9189-CRIStAL, France · Independent Researcher
JaxMARL: Multi-Agent RL Environments and Algorithms in JAX
Nov 16, 2023Alexander Rutherford, Benjamin Ellis, Matteo Gallici +18RL Benchmarks Reinforcement Learning
University of Oxford · Universitat Politècnica de Catalunya · University College London +2

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Reinforcement learning for hardware acceleration and system-level optimization on FPGA and integrated-circuit platforms: A survey

Hamza Kheddar

LSEA Laboratory, Department of Electrical Engineering, University of Medea, 26000, Algeria

GPU-Accelerated RL Reinforcement Learning FPGA Acceleration

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Reinforcement learning (RL) enables adaptive and autonomous decision-making in embedded, edge, and heterogeneous computing systems. Field-programmable gate arrays (FPGAs) and integrated circuits (ICs) offer low latency, energy efficiency, and architectural flexibility for RL deployment, while introducing challenges in resource constraints, implementation complexity, and hardware-algorithm co-design. This review surveys more than 100 studies on RL implementations across FPGA and IC platforms, emphasizing architectures, implementation strategies, performance evaluation, and applications. The literature is organized by RL family, including model-based Markov decision processes, classical value-based methods, deep RL, actor-critic, policy-gradient, and multi-agent approaches. We further classify studies by abstraction level, hardware realization strategy, and application domain, examining how RL algorithms map onto reconfigurable and fixed hardware. Unlike surveys restricted to particular algorithms or platforms, this work provides a unified analysis of FPGA and IC implementations, covering acceleration, reconfigurable architectures, energy-aware control, and system-level optimization. Comparative assessments consider resource utilization, latency, throughput, power consumption, and reported performance gains. Finally, we identify recurring architectural patterns, practical limitations, open challenges, and research directions for efficient RL-driven hardware systems. The survey offers a consolidated reference for researchers developing and evaluating RL solutions on specialized hardware.