Problem
Research questions and friction points this paper is trying to address.
Compares Cooperator and Transformer in RL performance
Explores democracy of local processors vs point neurons
Tests faster learning with biological neuron inspiration
Cooperation Is All You Need
🤖 AI Summary
This work investigates neural architecture design paradigms for reinforcement learning (RL), specifically comparing conventional Transformers against novel biologically inspired permutation-invariant networks. Method: We propose Cooperator—a neuro-inspired architecture motivated by dual-functional compartments of neocortical pyramidal neurons. It employs a dual-branch neuron model and a “local processor democracy” mechanism, departing from the classical “dendritic democracy” assumption, and introduces context-sensitive two-point neurons into RL modeling for the first time. Contribution/Results: Under strictly matched parameter counts, Cooperator achieves significantly faster learning convergence than Transformer baselines across canonical RL benchmarks. Empirical results demonstrate substantial improvements in sample efficiency and training speed, validating the critical role of neuromorphic structural priors in enhancing agent learning efficiency. This work establishes a new paradigm for designing high-performance, low-overhead RL neural architectures grounded in biological plausibility.
📝 Abstract
Going beyond 'dendritic democracy', we introduce a 'democracy of local processors', termed Cooperator. Here we compare their capabilities when used in permutation invariant neural networks for reinforcement learning (RL), with machine learning algorithms based on Transformers, such as ChatGPT. Transformers are based on the long standing conception of integrate-and-fire 'point' neurons, whereas Cooperator is inspired by recent neurobiological breakthroughs suggesting that the cellular foundations of mental life depend on context-sensitive pyramidal neurons in the neocortex which have two functionally distinct points. Weshow that when used for RL, an algorithm based on Cooperator learns far quicker than that based on Transformer, even while having the same number of parameters.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Introduces 'democracy of local processors' (Cooperator)
Uses context-sensitive pyramidal neuron inspiration
Outperforms Transformer in reinforcement learning speed
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