mlsanityData to 14 Oct 2026

Implementing Online Reinforcement Learning with Clustering Neural Networks

arXiv 2402.18472 ↗First posted 28 Feb 2024cs.NEcited by 0

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

Abstract (arXiv)

An agent employing reinforcement learning takes inputs (state variables) from an environment and performs actions that affect the environment in order to achieve some objective. Rewards (positive or negative) guide the agent toward improved future actions. This paper builds on prior clustering neural network research by constructing an agent with biologically plausible neo-Hebbian three-factor synaptic learning rules, with a reward signal as the third factor (in addition to pre- and post-synaptic spikes). The classic cart-pole problem (balancing an inverted pendulum) is used as a running example throughout the exposition. Simulation results demonstrate the efficacy of the approach, and the proposed method may eventually serve as a low-level component of a more general method.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 15 references that are in the map. First posted on arXiv 28 Feb 2024.

Builds on

5 of 15 references.

References · 5

  1. 2018Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of neoHebbian Three-Factor Learning Rulescited by 67
  2. 2015Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortexcited by 45
  3. 2018Comparison of Reinforcement Learning algorithms applied to the Cart Pole problemcited by 13
  4. 2016Encoding Data for HTM Systemscited by 7
  5. 2023Neuromorphic Online Clustering and Classificationcited by 2

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Harnessing Data from Clustered LQR Systems: Personalized and Collaborative Policy Optimization
  2. 2024Large Scale Constrained Clustering With Reinforcement Learning
  3. 2025Asymptotically optimal reinforcement learning in Block Markov Decision Processes
  4. 2024Exploring the Edges of Latent State Clusters for Goal-Conditioned Reinforcement Learning
  5. 2025Clustering-Based Weight Orthogonalization for Stabilizing Deep Reinforcement Learning
  6. 2025Three-Factor Learning in Spiking Neural Networks: An Overview of Methods and Trends from a Machine Learning Perspective
  7. 2025Policy-Based Trajectory Clustering in Offline Reinforcement Learning
  8. 2024ClusterComm: Discrete Communication in Decentralized MARL using Internal Representation Clustering