পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| গভীর শক্তিশালীকরণ শিক্ষা× | পুনরাবৃত্ত নিউরাল নেটওয়ার্ক× | |
|---|---|---|
| ক্ষেত্র | গভীর শিখন | গভীর শিখন |
| পরিবার | Machine learning | Machine learning |
| উদ্ভবের বছর≠ | 2015 | 1986–1990 |
| প্রবর্তক≠ | Mnih, V. et al. (DQN) | Rumelhart, D. E.; Elman, J. L. |
| ধরন≠ | Sequential decision-making (agent–environment interaction) | Sequential neural network |
| মৌলিক উৎস≠ | Mnih, V. et al. (2015). Human-Level Control through Deep Reinforcement Learning. Nature, 518, 529–533. DOI ↗ | Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. DOI ↗ |
| অপর নাম≠ | Derin Pekiştirmeli Öğrenme (DQN / PPO / A3C), derin pekiştirmeli öğrenme, deep RL, DRL | RNN, Elman network, Jordan network, simple recurrent network |
| সম্পর্কিত≠ | 4 | 3 |
| সারসংক্ষেপ≠ | Deep Reinforcement Learning combines neural networks with reinforcement learning so an agent learns by interacting with an environment, popularised by Mnih and colleagues' 2015 Nature work on human-level Atari control. Instead of learning from a fixed labelled dataset, the agent takes actions, observes rewards, and gradually shapes a policy that maximises long-run return. | A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models. |
| ScholarGateডেটাসেট ↗ |
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