Actor-critic methods have achieved significant success in many challenging applications.
However, its finite-time convergence is still poorly understood in the most practical single …

Policy iteration (PI) is a fundamental policy search algorithm in standard reinforcement
learning (RL) setting, which can be shown to converge to an optimal policy by policy …

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 …

$ Q $-learning is one of the most fundamental reinforcement learning (RL) algorithms.
Despite its widespread success in various applications, it is prone to overestimation bias in …

TD-learning is a fundamental algorithm in the field of reinforcement learning (RL), that is
employed to evaluate a given policy by estimating the corresponding value function for a …

(RL). The agent acts greedily with respect to an estimate of the value plus what can be seen
as a value bonus. The value bonus can be learned by estimating a value function on reward …