方法对比
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| 强化学习× | 策略梯度方法× | 循环神经网络× | |
|---|---|---|---|
| 领域≠ | 深度学习 | 机器学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning | Machine learning |
| 起源年份≠ | 1950s–1998 | 1992 | 1986–1990 |
| 提出者≠ | Sutton, R. S. & Barto, A. G. (formalised); Bellman, R. (foundations) | Ronald Williams (REINFORCE); Sutton et al. (policy gradient theorem) | Rumelhart, D. E.; Elman, J. L. |
| 类型≠ | Sequential decision-making framework | Policy-based reinforcement learning | Sequential neural network |
| 开创性文献≠ | Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN: 978-0-262-03924-6 | Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8(3–4), 229–256. DOI ↗ | Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. DOI ↗ |
| 别名 | RL, reward-based learning, trial-and-error learning, policy optimization | REINFORCE, actor-critic, policy optimization, politika gradyanı | RNN, Elman network, Jordan network, simple recurrent network |
| 相关≠ | 2 | 4 | 3 |
| 摘要≠ | Reinforcement Learning (RL) is a framework in which an agent learns to make sequential decisions by interacting with an environment, receiving scalar reward signals, and updating a policy to maximise cumulative future reward. Unlike supervised learning, no labeled examples are provided; the agent discovers optimal behavior entirely through experience and delayed feedback. | 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. |
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