Off-Policy TD Control

Clipped Double Q-learning

Introduced by Fujimoto et al. in Addressing Function Approximation Error in Actor-Critic Methods

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 Methods


Paper Code Results Date Stars


Task Papers Share
Reinforcement Learning (RL) 56 43.75%
Continuous Control 25 19.53%
OpenAI Gym 9 7.03%
Decision Making 5 3.91%
Autonomous Driving 4 3.13%
Offline RL 3 2.34%
Meta-Learning 3 2.34%
Benchmarking 3 2.34%
D4RL 2 1.56%


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