A Unified Cognitive Learning Framework for Adapting to Dynamic Environment and Tasks

• URL: http://arxiv.org/abs/2106.00501v1
• Date: Tue, 1 Jun 2021 14:08:20 GMT
• Title: A Unified Cognitive Learning Framework for Adapting to Dynamic Environment and Tasks
• Authors: Qihui Wu, Tianchen Ruan, Fuhui Zhou, Yang Huang, Fan Xu, Shijin Zhao, Ya Liu, and Xuyang Huang
• Abstract summary: We propose a unified cognitive learning (CL) framework for the dynamic wireless environment and tasks. We show that our proposed CL framework has three advantages, namely, the capability of adapting to the dynamic environment and tasks, the self-learning capability and the capability of 'good money driving out bad money' by taking modulation recognition as an example.
• Score: 19.459770316922437
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many machine learning frameworks have been proposed and used in wireless communications for realizing diverse goals. However, their incapability of adapting to the dynamic wireless environment and tasks and of self-learning limit their extensive applications and achievable performance. Inspired by the great flexibility and adaptation of primate behaviors due to the brain cognitive mechanism, a unified cognitive learning (CL) framework is proposed for the dynamic wireless environment and tasks. The mathematical framework for our proposed CL is established. Using the public and authoritative dataset, we demonstrate that our proposed CL framework has three advantages, namely, the capability of adapting to the dynamic environment and tasks, the self-learning capability and the capability of 'good money driving out bad money' by taking modulation recognition as an example. The proposed CL framework can enrich the current learning frameworks and widen the applications.

Related papers

• Demonstrating the Continual Learning Capabilities and Practical Application of Discrete-Time Active Inference [0.0]
  Active inference is a mathematical framework for understanding how agents interact with their environments. In this paper, we present a continual learning framework for agents operating in discrete time environments. We demonstrate the agent's ability to relearn and refine its models efficiently, making it suitable for complex domains like finance and healthcare.
  arXiv  Detail & Related papers  (2024-09-30T21:18:46Z)
• A Unified and General Framework for Continual Learning [58.72671755989431]
  Continual Learning (CL) focuses on learning from dynamic and changing data distributions while retaining previously acquired knowledge. Various methods have been developed to address the challenge of catastrophic forgetting, including regularization-based, Bayesian-based, and memory-replay-based techniques. This research aims to bridge this gap by introducing a comprehensive and overarching framework that encompasses and reconciles these existing methodologies.
  arXiv  Detail & Related papers  (2024-03-20T02:21:44Z)
• Adaptive Ensemble Learning: Boosting Model Performance through Intelligent Feature Fusion in Deep Neural Networks [0.0]
  We present an Adaptive Ensemble Learning framework that aims to boost the performance of deep neural networks. The framework integrates ensemble learning strategies with deep learning architectures to create a more robust and adaptable model. By leveraging intelligent feature fusion methods, the framework generates more discriminative and effective feature representations.
  arXiv  Detail & Related papers  (2023-04-04T21:49:49Z)
• Unsupervised Domain Adaptation with Dynamics-Aware Rewards in Reinforcement Learning [28.808933152885874]
  Unconditioned reinforcement learning aims to acquire skills without prior goal representations. The intuitive approach of training in another interaction-rich environment disrupts the trained skills in the target environment. We propose an unsupervised domain adaptation method to identify and acquire skills across dynamics.
  arXiv  Detail & Related papers  (2021-10-25T14:40:48Z)
• Learning Multi-Objective Curricula for Deep Reinforcement Learning [55.27879754113767]
  Various automatic curriculum learning (ACL) methods have been proposed to improve the sample efficiency and final performance of deep reinforcement learning (DRL) In this paper, we propose a unified automatic curriculum learning framework to create multi-objective but coherent curricula. In addition to existing hand-designed curricula paradigms, we further design a flexible memory mechanism to learn an abstract curriculum.
  arXiv  Detail & Related papers  (2021-10-06T19:30:25Z)
• Backprop-Free Reinforcement Learning with Active Neural Generative Coding [84.11376568625353]
  We propose a computational framework for learning action-driven generative models without backpropagation of errors (backprop) in dynamic environments. We develop an intelligent agent that operates even with sparse rewards, drawing inspiration from the cognitive theory of planning as inference. The robust performance of our agent offers promising evidence that a backprop-free approach for neural inference and learning can drive goal-directed behavior.
  arXiv  Detail & Related papers  (2021-07-10T19:02:27Z)
• A Framework to Counteract Suboptimal User-Behaviors in Exploratory Learning Environments: an Application to MOOCs [1.1421942894219896]
  We focus on a data-driven user-modeling framework that uses logged interaction data to learn which behavioral or activity patterns should trigger help. We present a novel application of this framework to Massive Open Online Courses (MOOCs), a form of exploratory environment.
  arXiv  Detail & Related papers  (2021-06-14T16:16:33Z)
• Generative Adversarial Reward Learning for Generalized Behavior Tendency Inference [71.11416263370823]
  We propose a generative inverse reinforcement learning for user behavioral preference modelling. Our model can automatically learn the rewards from user's actions based on discriminative actor-critic network and Wasserstein GAN.
  arXiv  Detail & Related papers  (2021-05-03T13:14:25Z)
• Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective [52.497514255040514]
  This work develops a new approach that enables data-driven methods to continuously learn and optimize resource allocation strategies in a dynamic environment. We propose to build the notion of continual learning into wireless system design, so that the learning model can incrementally adapt to the new episodes. Our design is based on a novel bilevel optimization formulation which ensures certain fairness" across different data samples.
  arXiv  Detail & Related papers  (2021-05-03T07:23:39Z)
• Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment [55.91291559442884]
  This work develops a methodology that enables data-driven methods to continuously learn and optimize in a dynamic environment. We propose to build the notion of continual learning into the modeling process of learning wireless systems. Our design is based on a novel min-max formulation which ensures certain fairness" across different data samples.
  arXiv  Detail & Related papers  (2020-11-16T08:24:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.