# Curriculum Learning for Goal-Oriented Semantic Communications with a Common Language arXiv: https://arxiv.org/abs/2204.10429 ยท HTML: https://mlsanity.com/paper/2204.10429/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Builds on 19 of its 31 references that are in the map. - First posted on arXiv 21 Apr 2022. ## Builds on (most cited first, 19 of 19) - [Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey](https://mlsanity.com/paper/2003.04960/) (2020) - [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020) - [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019) - [6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications](https://mlsanity.com/paper/2011.14844/) (2020) - [Semantic Communications: Principles and Challenges](https://mlsanity.com/paper/2201.01389/) (2021) - [Task-Oriented Multi-User Semantic Communications](https://mlsanity.com/paper/2112.10255/) (2021) - [A Lite Distributed Semantic Communication System for Internet of Things](https://mlsanity.com/paper/2007.11095/) (2020) - [Deep Learning for Joint Source-Channel Coding of Text](https://mlsanity.com/paper/1802.06832/) (2018) - [Task-Oriented Multi-User Semantic Communications for VQA Task](https://mlsanity.com/paper/2108.07357/) (2021) - [Semantics-Native Communication with Contextual Reasoning](https://mlsanity.com/paper/2108.05681/) (2021) - [Semantic Communication with Adaptive Universal Transformer](https://mlsanity.com/paper/2108.09119/) (2021) - [Semantic Communication Meets Edge Intelligence](https://mlsanity.com/paper/2202.06471/) (2022) - [Semantic-Effectiveness Filtering and Control for Post-5G Wireless Connectivity](https://mlsanity.com/paper/1907.02441/) (2019) - [Reinforcement Learning-powered Semantic Communication via Semantic Similarity](https://mlsanity.com/paper/2108.12121/) (2021) - [Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication](https://mlsanity.com/paper/2105.10716/) (2021) - [Common Language for Goal-Oriented Semantic Communications: A Curriculum Learning Framework](https://mlsanity.com/paper/2111.08051/) (2021) - [Cognitive Semantic Communication Systems Driven by Knowledge Graph](https://mlsanity.com/paper/2202.11958/) (2022) - [An Indirect Rate-Distortion Characterization for Semantic Sources: General Model and the Case of Gaussian Observation](https://mlsanity.com/paper/2201.12477/) (2022) - [Semantic-Aware Collaborative Deep Reinforcement Learning Over Wireless Cellular Networks](https://mlsanity.com/paper/2111.12064/) (2021) ## Built on by (most cited first, 14 of 14) - [Less Data, More Knowledge: Building Next Generation Semantic Communication Networks](https://mlsanity.com/paper/2211.14343/) (2022) - [Neuro-Symbolic Causal Reasoning Meets Signaling Game for Emergent Semantic Communications](https://mlsanity.com/paper/2210.12040/) (2022) - [Semantic Channel Equalizer: Modelling Language Mismatch in Multi-User Semantic Communications](https://mlsanity.com/paper/2308.03789/) (2023) - [Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach](https://mlsanity.com/paper/2304.12502/) (2023) - [OFDM-Based Digital Semantic Communication with Importance Awareness](https://mlsanity.com/paper/2401.02178/) (2024) - [Neuro-Symbolic Artificial Intelligence (AI) for Intent based Semantic Communication](https://mlsanity.com/paper/2205.10768/) (2022) - [Agent-driven Generative Semantic Communication with Cross-Modality and Prediction](https://mlsanity.com/paper/2404.06997/) (2024) - [Reasoning with the Theory of Mind for Pragmatic Semantic Communication](https://mlsanity.com/paper/2311.18224/) (2023) - [Disentangling Learnable and Memorizable Data via Contrastive Learning for Semantic Communications](https://mlsanity.com/paper/2212.09071/) (2022) - [Bayesian Inverse Contextual Reasoning for Heterogeneous Semantics-Native Communication](https://mlsanity.com/paper/2306.06403/) (2023) - [IRS-Enhanced Secure Semantic Communication Networks: Cross-Layer and Context-Awared Resource Allocation](https://mlsanity.com/paper/2411.01821/) (2024) - [Semantic-Preserving Augmentation for Robust Image-Text Retrieval](https://mlsanity.com/paper/2303.05692/) (2023) - [Joint Semantic-Native Communication and Inference via Minimal Simplicial Structures](https://mlsanity.com/paper/2308.16789/) (2023) - [TSCL:Multi-party loss Balancing scheme for deep learning Image steganography based on Curriculum learning](https://mlsanity.com/paper/2504.18348/) (2025) ## Abstract (arXiv) Goal-oriented semantic communication will be a pillar of next-generation wireless networks. Despite significant recent efforts in this area, most prior works are focused on specific data types (e.g., image or audio), and they ignore the goal and effectiveness aspects of semantic transmissions. In contrast, in this paper, a holistic goal-oriented semantic communication framework is proposed to enable a speaker and a listener to cooperatively execute a set of sequential tasks in a dynamic environment. A common language based on a hierarchical belief set is proposed to enable semantic communications between speaker and listener. The speaker, acting as an observer of the environment, utilizes the beliefs to transmit an initial description of its observation (called event) to the listener. The listener is then able to infer on the transmitted description and complete it by adding related beliefs to the transmitted beliefs of the speaker. As such, the listener reconstructs the observed event based on the completed description, and it then takes appropriate action in the environment based on the reconstructed event. An optimization problem is defined to determine the perfect and abstract description of the events while minimizing the transmission and inference costs with constraints on the task execution time and belief efficiency. Then, a novel bottom-up curriculum learning (CL) framework based on reinforcement learning is proposed to solve the optimization problem and enable the speaker and listener to gradually identify the structure of the belief set and the perfect and abstract description of the events. Simulation results show that the proposed CL method outperforms traditional RL in terms of convergence time, task execution cost and time, reliability, and belief efficiency.