Cultivating Expertise in Deep and Reinforcement Learning Principles
Chilakalapudi Malathi, J Sheela · 2024
This abstract provides an insightful exploration of these foundational concepts, beginning with reinforcement learning, which addresses dynamic decision-making and emphasizes agent–environment interaction to optimize cumulative rewards. It uncovers the core principles, underlying mechanisms, key algorithms, and practical applications in various industries while shedding light on the societal implications and ethical considerations linked to this technology. Shifting focus to deep learning, this abstract showcases the use of neural networks with multiple layers to extract intricate patterns from complex data, resulting in transformative advancements in image recognition, natural language processing, and autonomous systems. By comprehending these principles, researchers and practitioners can harness the power of reinforcement and deep learning, thereby contributing to the continual evolution of AI and its real-world problem-solving capabilities.