Hardware-Accelerated MaxRL: Scaling Maximum Likelihood RL with FPGAs and Edge Devices

TL;DR: Let’s turbocharge MaxRL on custom hardware like FPGAs, making it practical for real-world robotics and control where fast learning is essential. Prototype and benchmark a hardware-accelerated MaxRL implementation using Zynq SoC or similar platforms.

Research Question: Can MaxRL be effectively accelerated on specialized hardware (e.g., FPGAs), and what are the performance, energy, and real-time benefits for robotics and edge RL applications?

Hypothesis: A carefully pipelined and parallelized implementation of MaxRL on FPGAs will yield significant speedups and energy savings, enabling its deployment in latency-sensitive or resource-constrained environments.

Experiment Plan: - Map MaxRL’s compute-intensive steps (sampling, gradient estimation) onto FPGA logic.

• Measure speedup, throughput, and energy efficiency vs. CPU/GPU baselines on real-world navigation or control tasks.
• Analyze trade-offs in resource utilization and accuracy.

References:

• Tajwar, F., et al. (2026). Maximum Likelihood Reinforcement Learning.
• Wang, L., Jiang, Y., Shao, L., Kong, Z., & Liao, Z. (2025). Testing the acceleration and optimization of deep reinforcement learning algorithms in intelligent control using Zynq SoC platform.