A Basal Ganglia–Inspired Reinforcement Learning Framework Using Dopamine-Modulated Plasticity

This paper presents a Basal Ganglia–inspired reinforcement learning (RL) model that unifies biological learning mechanisms with computational intelligence. The proposed framework emulates the functional dynamics of the Basal Ganglia through a three-layer neural architecture comprising cortical, striatal, and action-selection modules. Learning is governed by a dopamine-modulated synaptic plasticity rule, where the Reward Prediction Error (RPE) serves as the core teaching signal to adapt corticostriatal synaptic weights. A synthetic environment was programmatically constructed to generate discrete state–action–reward sequences, allowing the agent to learn through iterative trial-based interactions. The model employs soft-max based probabilistic action selection and updates synaptic weights according to the magnitude and polarity of the RPE signal, effectively replicating long-term potentiation (LTP) and long-term depression (LTD) observed in biological neurons. Experimental results demonstrate progressive improvement in cumulative rewards, stable RPE convergence, and consistent policy refinement across training episodes. The proposed model bridges the gap between computational reinforcement learning and neural decision-making, offering a biologically interpretable and efficient framework for adaptive behaviour modelling in brain-inspired artificial intelligence systems.