Deep Reinforcement Learning: Bridging the Gap with Neural Networks
Keywords:
Deep learning, Reinforcement learning, Artificial Intelligence, Neural NetworkAbstract
Deep Reinforcement Learning (DRL) represents a paradigm shift in artificial intelligence, combining the strengths of neural networks with the decision-making process of reinforcement learning. This paper explores the symbiotic relationship between DRL and neural networks, elucidating their collaborative potential in addressing complex problems. Neural networks serve as powerful function approximators, enabling DRL systems to efficiently handle high-dimensional state spaces and intricate relationships within raw input data. The key components of DRL, including value and policy networks, leverage neural networks to enhance learning efficiency. Applications span diverse domains, from robotics and game playing to autonomous systems. However, challenges such as sample inefficiency and interpretability persist. This abstract encapsulates the transformative synergy between DRL and neural networks, showcasing their potential to bridge gaps in traditional problem-solving paradigms and propel the field toward novel applications and advancements. As ongoing research addresses challenges, this collaboration promises to unlock new frontiers in intelligent decision-making and problem-solving. Deep Reinforcement Learning (DRL) represents a groundbreaking paradigm in artificial intelligence, seamlessly marrying the power of neural networks with reinforcement learning principles. This paper explores the synthesis of these two domains, elucidating the synergies that arise when deep neural networks serve as function approximators for value and policy functions in reinforcement learning tasks. The essence of DRL lies in its capacity to handle high-dimensional input spaces, learn hierarchical representations, and autonomously discover complex strategies through interaction with the environment. The paper also surveys key advancements and breakthroughs, highlighting the evolution of DRL from early successes to recent state-of-the-art methodologies. Moreover, this paper investigates the applicability of DRL across diverse domains, including robotics, gaming, and decision-making problems.
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