LearningGroup: A Real-Time Sparse Training on FPGA via Learnable Weight Grouping for Multi-Agent Reinforcement Learning

no code implementations • 29 Oct 2022 • Je Yang, JaeUk Kim, Joo-Young Kim

Unlike supervised model or single-agent reinforcement learning, which actively exploits network pruning, it is obscure that how pruning will work in multi-agent reinforcement learning with its cooperative and interactive characteristics.

Multi-agent Reinforcement Learning Network Pruning +3

FIXAR: A Fixed-Point Deep Reinforcement Learning Platform with Quantization-Aware Training and Adaptive Parallelism

no code implementations • 24 Feb 2021 • Je Yang, Seongmin Hong, Joo-Young Kim

In this paper, we present a deep reinforcement learning platform named FIXAR which employs fixed-point data types and arithmetic units for the first time using a SW/HW co-design approach.

Quantization Reinforcement Learning (RL)