Population-coding And Dynamic-neurons Improved Spiking Actor Network For Reinforcement Learning

Abstract

With the Deep Neural Networks (DNNs) as a powerful function approximator, Deep Reinforcement Learning (DRL) has been excellently demonstrated on robotic control tasks. Compared to DNNs with vanilla artificial neurons, the biologically plausible Spiking Neural Network (SNN) contains a diverse population of spiking neurons, making it naturally powerful on state representation with spatial and temporal information. Based on a hybrid learning framework, where a spike actor-network infers actions from states and a deep critic network evaluates the actor, we propose a Population-coding and Dynamic-neurons improved Spiking Actor Network (PDSAN) for efficient state representation from two different scales: input coding and neuronal coding. For input coding, we apply population coding with dynamically receptive fields to directly encode each input state component. For neuronal coding, we propose different types of dynamic-neurons (containing 1st-order and 2nd-order neuronal dynamics) to describ

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyzhang2021population

Related papers

• Deep Reinforcement Learning With Population-coded Spiking Neural Network For Continuous Control (2020)0.00
• S2act: Simple Spiking Actor (2026)0.00
• Fully Spiking Actor Network With Intra-layer Connections For Reinforcement Learning (2024)0.00
• Dynamic Reinforcement Learning For Actors (2025)0.00
• Deep Reinforcement Learning With Spiking Q-learning (2022)0.00
• Learning To Control Dynamical Agents Via Spiking Neural Networks And Metropolis-hastings Sampling (2025)0.00
• Human-level Control Through Directly-trained Deep Spiking Q-networks (2021)12.40
• Reinforcement Learning With A Network Of Spiking Agents (2019)0.00