Tuning Synaptic Connections instead of Weights by Genetic Algorithm in Spiking Policy Network
This addresses energy efficiency in robotic control tasks, offering a biologically-inspired alternative to DRL, though it is incremental as it builds on existing spiking and genetic algorithm approaches.
The paper tackled the energy inefficiency of deep reinforcement learning by optimizing a spiking policy network using a genetic algorithm to tune synaptic connections instead of weights, achieving the same performance as mainstream DRL methods with significantly higher energy efficiency.
Learning from interaction is the primary way that biological agents acquire knowledge about their environment and themselves. Modern deep reinforcement learning (DRL) explores a computational approach to learning from interaction and has made significant progress in solving various tasks. However, despite its power, DRL still falls short of biological agents in terms of energy efficiency. Although the underlying mechanisms are not fully understood, we believe that the integration of spiking communication between neurons and biologically-plausible synaptic plasticity plays a prominent role in achieving greater energy efficiency. Following this biological intuition, we optimized a spiking policy network (SPN) using a genetic algorithm as an energy-efficient alternative to DRL. Our SPN mimics the sensorimotor neuron pathway of insects and communicates through event-based spikes. Inspired by biological research showing that the brain forms memories by creating new synaptic connections and rewiring these connections based on new experiences, we tuned the synaptic connections instead of weights in the SPN to solve given tasks. Experimental results on several robotic control tasks demonstrate that our method can achieve the same level of performance as mainstream DRL methods while exhibiting significantly higher energy efficiency.