Survey maps reinforcement learning implementations on FPGAs and ICs

This survey paper reviews over 100 studies on the implementation of reinforcement learning (RL) algorithms on field-programmable gate arrays (FPGAs) and integrated circuits (ICs). It categorizes research by RL algorithm family, hardware realization strategy, and application domain, offering a unified analysis of how RL maps onto reconfigurable and fixed hardware. The paper examines architectural patterns, practical limitations, and future research directions for efficient RL-driven hardware systems, providing a consolidated reference for researchers. AI

IMPACT Provides a consolidated reference for researchers developing and evaluating RL solutions on specialized hardware.

RANK_REASON The item is a survey paper published on arXiv in the cs.LG category. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• actor-critic algorithm
• arXiv
• cs.LG
• field-programmable gate array
• integrated circuit
• Markov decision processes
• multi-agent system
• policy-gradient method
• reinforcement learning

• paper
• infra

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