• Ikvinderpal Singh

Deep Reinforcement Learning (DRL), which combines the strengths of deep learning and reinforcement learning, has emerged as a potent paradigm for developing intelligent agents capable of autonomous decision-making in high-dimensional, complex environments. This survey provides a detailed examination of the fundamental methodologies and recent advances in DRL. We review value-based methods, such as Deep Q-Networks (DQNs) and their extensions, which approximate action-value functions to guide decision-making. Policy-based methods, including REINFORCE and Actor-Critic approaches, are also discussed for their ability to directly optimize the agent's policy in continuous action spaces. The survey further explores model-based DRL, which incorporates environment modeling for data efficiency, as well as hierarchical and multi-agent approaches that enhance the scalability and robustness of DRL in multi-faceted applications. Beyond individual algorithms, we examine key architectural advancements in DRL, including the use of Convolutional Neural Networks (CNNs) for spatial data, Recurrent Neural Networks (RNNs) for sequential processing, and Transformers for managing long-range dependencies. These architectures have significantly expanded the applicability of DRL across diverse domains. From autonomous driving and robotics to natural language processing and healthcare, DRL has demonstrated its potential to transform industries through adaptive and intelligent decision-making.

Deep Reinforcement Learning (DRL); Deep Q-Networks (DQNs); Convolutional Neural Networks (CNNs); Recurrent Neural Networks (RNNs)

"Algorithms and Architectures of Deep Reinforcement Learning: A survey", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.i607-i614, March-2023, Available :http://www.jetir.org/papers/JETIR2303886.pdf

"Algorithms and Architectures of Deep Reinforcement Learning: A survey", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. ppi607-i614, March-2023, Available at : http://www.jetir.org/papers/JETIR2303886.pdf