Video game development is getting increasingly effective as AI paradigms advance. Deep
Reinforcement Learning (DRL) is a promising artificial intelligence (AI) approach. It …

The ensemble method is a promising way to mitigate the overestimation issue in Q-learning,
where multiple function approximators are used to estimate the action values. It is known …

Double Q-learning is a classical method for reducing overestimation bias, which is caused
by taking maximum estimated values in the Bellman operation. Its variants in the deep Q …

Q-learning is a stochastic approximation version of the classic value iteration. The literature
has established that Q-learning suffers from both maximization bias and slower …

In multi-agent reinforcement learning (MARL), the low quality of value function and the
estimation bias and variance in value function decomposition (VFD) are critical challenges …

It is a longstanding problem that Q-learning suffers from the overestimation bias. This issue
originates from the fact that Q-learning uses the expectation of maximum Q-value to …

A variety of algorithms have been proposed to address the long-standing overestimation
bias problem of Q-learning. Reducing this overestimation bias may lead to an …

A variety of algorithms has been proposed to mitigate the overestimation bias of Q-learning.
These algorithms reduce the estimation of maximum Q-value, ie, homogeneous update. As …

In Reinforcement Learning the Q-learning algorithm provably converges to the optimal
solution. However, as others have demonstrated, Q-learning can also overestimate the …

Total freight transportation is increasing year by year, and freight railway energy
consumption is also increasing. This paper studies the energy-saving speed curve of heavy …