방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| Q-러닝× | 정책 경사도 방법× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도 | 1992 | 1992 |
| 창시자≠ | Christopher Watkins & Peter Dayan | Ronald Williams (REINFORCE); Sutton et al. (policy gradient theorem) |
| 유형≠ | Model-free reinforcement-learning control algorithm | Policy-based reinforcement learning |
| 원전≠ | Watkins, C. J. C. H., & Dayan, P. (1992). Q-learning. Machine Learning, 8(3–4), 279–292. DOI ↗ | Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8(3–4), 229–256. DOI ↗ |
| 별칭 | Q-learning algorithm, tabular Q-learning, off-policy TD control, Q-öğrenme | REINFORCE, actor-critic, policy optimization, politika gradyanı |
| 관련≠ | 3 | 4 |
| 요약≠ | Q-learning, introduced by Christopher Watkins and Peter Dayan in 1992, is a model-free reinforcement-learning algorithm that learns the value of taking each action in each state — the Q-function — purely from experience, without a model of the environment. It is off-policy: it learns the optimal action-values while following an exploratory behaviour policy, and under standard conditions it provably converges to the optimal policy. | 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. |
| ScholarGate데이터셋 ↗ |
|
|