Q-Learning is an off-policy temporal difference control algorithm:
$$Q\left(S_{t}, A_{t}\right) \leftarrow Q\left(S_{t}, A_{t}\right) + \alpha\left[R_{t+1} + \gamma\max_{a}Q\left(S_{t+1}, a\right) - Q\left(S_{t}, A_{t}\right)\right] $$
The learned action-value function $Q$ directly approximates $q_{*}$, the optimal action-value function, independent of the policy being followed.
Source: Sutton and Barto, Reinforcement Learning, 2nd Edition
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Task | Papers | Share |
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Reinforcement Learning (RL) | 206 | 34.28% |
Decision Making | 48 | 7.99% |
Multi-agent Reinforcement Learning | 27 | 4.49% |
Offline RL | 26 | 4.33% |
Management | 26 | 4.33% |
Atari Games | 17 | 2.83% |
OpenAI Gym | 13 | 2.16% |
Continuous Control | 11 | 1.83% |
Imitation Learning | 11 | 1.83% |
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