Gaussian-Sigmoid Reinforcement Transistors: Resolving Exploration-Exploitation Trade-Off Through Gate Voltage-Controlled Activation Functions

Abstract

Reinforcement learning (RL) relies on Gaussian and sigmoid functions to balance exploration and exploitation, but implementing these functions in hardware typically requires iterative computations, increasing power and circuit complexity. Here, Gaussian-sigmoid reinforcement transistors (GS-RTs) are reported that integrate both activation functions into a single device. The transistors feature a vertical n-p-i-p heterojunction stack composed of a-IGZO and DNTT, with asymmetric source-drain contacts and a parylene interlayer that enables voltage-tunable transitions between sigmoid, Gaussian, and mixed responses. This architecture emulates the behavior of three transistors in one, reducing the required circuit complexity from dozens of transistors to fewer than a few. The GS-RT exhibits a peak current of 5.95 mu A at VG = -17 V and supports nonlinear transfer characteristics suited for neuromorphic computing. In a multi-armed bandit task, GS-RT-based RL policies demonstrate 20% faster convergence and 30% higher final reward compared to conventional sigmoid- or Gaussian-based approaches. Extending this advantage further, GS-RT-based activation function in deep RL for cartpole balancing significantly outperforms the traditional ReLU-based activation function in terms of faster learning and tolerance to input perturbations.