পদ্ধতির তুলনা করুন

	পলিসি গ্রেডিয়েন্ট পদ্ধতি	পুনরাবৃত্ত নিউরাল নেটওয়ার্ক
ক্ষেত্র	যন্ত্র শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1992	1986–1990
প্রবর্তক	Ronald Williams (REINFORCE); Sutton et al. (policy gradient theorem)	Rumelhart, D. E.; Elman, J. L.
ধরন	Policy-based reinforcement learning	Sequential neural network
মৌলিক উৎস	Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8(3–4), 229–256.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
অপর নাম	REINFORCE, actor-critic, policy optimization, politika gradyanı	RNN, Elman network, Jordan network, simple recurrent network
সম্পর্কিত	4	3
সারসংক্ষেপ	Policy gradient methods are reinforcement-learning algorithms that optimize a parameterized policy directly by gradient ascent on the expected return, rather than learning action-values and acting greedily. Founded on Ronald Williams' 1992 REINFORCE algorithm and the policy gradient theorem of Sutton and colleagues (2000), they naturally handle stochastic and continuous action spaces and underpin modern actor-critic and deep-RL algorithms.	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.