Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Mbinu za Kielelezo cha Sera× | Ujifunzaji wa Kina wa Uimarishaji× | |
|---|---|---|
| Nyanja≠ | Ujifunzaji wa Mashine | Ujifunzaji wa Kina |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 1992 | 2015 |
| Mwanzilishi≠ | Ronald Williams (REINFORCE); Sutton et al. (policy gradient theorem) | Mnih, V. et al. (DQN) |
| Aina≠ | Policy-based reinforcement learning | Sequential decision-making (agent–environment interaction) |
| Chanzo asilia≠ | Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8(3–4), 229–256. DOI ↗ | Mnih, V. et al. (2015). Human-Level Control through Deep Reinforcement Learning. Nature, 518, 529–533. DOI ↗ |
| Majina mbadala≠ | REINFORCE, actor-critic, policy optimization, politika gradyanı | Derin Pekiştirmeli Öğrenme (DQN / PPO / A3C), derin pekiştirmeli öğrenme, deep RL, DRL |
| Zinazohusiana | 4 | 4 |
| Muhtasari≠ | 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. | 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. |
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