Deep Learning for autonomous intelligent agents

Autonomous intelligent agents possess the capability to autonomously perceive their environment and make decisions based on their prior knowledge or learning experiences. The field of Deep Learning (DL) has witnessed significant advancements in recent years, leading to the development of diverse algorithms applicable in various domains. By leveraging these algorithms, intelligent agents can extract and learn higher-level features from data, enabling quick and efficient perception. These algorithms find utility across domains such as visual perception for agents, modular learning frameworks, and collaborative learning scenarios in distributed deep learning. Despite the remarkable progress in DL algorithms, several challenges persist, including limited training data, data imbalance, scalability concerns, and data privacy considerations. The ability of intelligent agents to interpret visual data holds immense importance in applications where human assessment of data is time-consuming and labor-intensive. An illustrative example of such an application is disease detection in agriculture, where manual execution of this task can consume hours or even days for a single field. Consequently, training agents through deep learning algorithms and deploying them on edge devices for inference can provide effective solutions to such challenges. Intelligent agents are not confined to executing visual perception alone; they can also acquire knowledge through modular deep learning frameworks. In certain applications, such as robotics, intelligent agents must make decisions based on diverse visual and linguistic inputs from their environment. This task becomes even more challenging when dealing with cluttered and complex environments or when multiple factors, such as object manipulation, come into play, surpassing simple navigation requirements. Moreover, intelligent agents can collaborate to train a consensus model within a distributed setting. In decentralized deep learning, agents privately update their local deep learning models using their individual data and achieve consensus through peer-to-peer communication. This approach eliminates the need for a central server as in Federated Learning (FL). However, communication overhead remains a challenge in decentralized learning. Recent studies have proposed communication-efficient methods, although the reduction in communication time often comes at the expense of significant accuracy loss, particularly in large-scale networks. Additionally, a pertinent question arises regarding the efficacy of decentralized learning methods in practical scenarios, such as anomaly detection in agriculture. Specifically when the complexity intensifies when we encounter the non-uniform distribution of various plant genotypes within a field. This report aims to address the aforementioned challenges in different facets of employing intelligent agents. Firstly, an autonomous framework for disease detection in agriculture is investigated, targeting issues such as insufficient training data and data imbalance. In this context, a comparative analysis is performed between manually collected Single Nucleotide Polymorphisms (SNPs) and DL-executed SNPs to evaluate the performance of DL models. Secondly, a modular vision-language framework is proposed to tackle the challenges presented by the ALFRED dataset. This modular framework comprises six distinct modules, designed to enhance the agent's task completion abilities within this dataset. Thirdly, a communication-efficient graph-based method is introduced for decentralized deep learning, aiming to reduce communication time while simultaneously preserving high accuracy, even in large-scale networks. Fourthly, an investigation is conducted into detecting anomalies in agricultural fields using decentralized deep learning. This research aims to demonstrate the efficacy and generalizability of decentralized learning compared to local learning approaches, particularly in scenarios involving distributed genotypic differences across the field. Lastly, concluding remarks are provided, and future research directions are discussed to further advance the utilization of intelligent agents.