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
Paper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Reinforcement Learning (RL) | 200 | 33.73% |
Decision Making | 51 | 8.60% |
Management | 28 | 4.72% |
Multi-agent Reinforcement Learning | 27 | 4.55% |
Offline RL | 26 | 4.38% |
Atari Games | 16 | 2.70% |
OpenAI Gym | 13 | 2.19% |
Continuous Control | 12 | 2.02% |
Autonomous Driving | 10 | 1.69% |
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