Active Inference In Hebbian Learning Networks

Abstract

This work studies how brain-inspired neural ensembles equipped with local Hebbian plasticity can perform active inference (AIF) in order to control dynamical agents. A generative model capturing the environment dynamics is learned by a network composed of two distinct Hebbian ensembles: a posterior network, which infers latent states given the observations, and a state transition network, which predicts the next expected latent state given current state-action pairs. Experimental studies are conducted using the Mountain Car environment from the OpenAI gym suite, to study the effect of the various Hebbian network parameters on the task performance. It is shown that the proposed Hebbian AIF approach outperforms the use of Q-learning, while not requiring any replay buffer, as in typical reinforcement learning systems. These results motivate further investigations of Hebbian learning for the design of AIF networks that can learn environment dynamics without the need for revisiting past buf

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keysafa2023active

Related papers

• Deep Active Inference Agents Using Monte-carlo Methods (2020)0.00
• Deconstructing Deep Active Inference (2023)2.26
• Active Inference: Demystified And Compared (2019)15.98
• R-AIF: Solving Sparse-reward Robotic Tasks From Pixels With Active Inference And World Models (2024)4.52
• Active Inference And Reinforcement Learning: A Unified Inference On Continuous State And Action Spaces Under Partial Observability (2022)5.84
• Branching Time Active Inference: Empirical Study And Complexity Class Analysis (2021)6.77
• Contrastive Active Inference (2021)0.00
• Hebbian Synaptic Modifications In Spiking Neurons That Learn (2019)0.00