This survey examines 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 family, hardware realization strategy, and application domain, focusing on how RL algorithms are mapped to reconfigurable and fixed hardware. The review provides a unified analysis of FPGA and IC implementations, covering acceleration, energy-aware control, and system-level optimization, and identifies key challenges and future research directions for RL-driven hardware systems. AI
IMPACT Provides a consolidated reference for researchers developing and evaluating RL solutions on specialized hardware.
RANK_REASON This is a survey paper detailing research on reinforcement learning implementations. [lever_c_demoted from research: ic=1 ai=1.0]
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- Actor-critic algorithm
- end-to-end reinforcement learning
- field-programmable gate array
- Hugging Face
- Integrated circuits
- Markov decision processes
- multi-agent system
- policy-gradient method
- reinforcement learning
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