Clipped Double Q-learning is a variant on Double Q-learning that upper-bounds the less biased Q estimate $Q_{\theta_{2}}$ by the biased estimate $Q_{\theta_{1}}$. This is equivalent to taking the minimum of the two estimates, resulting in the following target update:
$$ y_{1} = r + \gamma\min_{i=1,2}Q_{\theta'_{i}}\left(s', \pi_{\phi_{1}}\left(s'\right)\right) $$
The motivation for this extension is that vanilla double Q-learning is sometimes ineffective if the target and current networks are too similar, e.g. with a slow-changing policy in an actor-critic framework.
Source: Addressing Function Approximation Error in Actor-Critic MethodsPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Reinforcement Learning (RL) | 61 | 41.22% |
Continuous Control | 27 | 18.24% |
OpenAI Gym | 9 | 6.08% |
Decision Making | 7 | 4.73% |
Autonomous Driving | 5 | 3.38% |
Offline RL | 3 | 2.03% |
Meta-Learning | 3 | 2.03% |
Benchmarking | 3 | 2.03% |
D4RL | 2 | 1.35% |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |