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Faster Non-asymptotic Convergence for Double Q-learning
This paper tackles the more challenging case of a constant learning rate, and develops new analytical tools that improve the existing convergence rate by orders of magnitude.
Double Q-learning: New Analysis and Sharper Finite-time Bound
Double Q-learning (Hasselt 2010) has gained significant success in practice due to its effectiveness in overcoming the overestimation issue of Q-learning.
Finite-Time Analysis for Double Q-learning
Although Q-learning is one of the most successful algorithms for finding the best action-value function (and thus the optimal policy) in reinforcement learning, its implementation often suffers from large overestimation of Q-function values incurred by random sampling.
Momentum Q-learning with Finite-Sample Convergence Guarantee
For the infinite state-action space case, we establish the convergence guarantee for MomentumQ with linear function approximations and Markovian sampling.
Analysis of Q-learning with Adaptation and Momentum Restart for Gradient Descent
In this paper, we first characterize the convergence rate for Q-AMSGrad, which is the Q-learning algorithm with AMSGrad update (a commonly adopted alternative of Adam for theoretical analysis).
Non-asymptotic Convergence of Adam-type Reinforcement Learning Algorithms under Markovian Sampling
Despite the wide applications of Adam in reinforcement learning (RL), the theoretical convergence of Adam-type RL algorithms has not been established.
CAN ALTQ LEARN FASTER: EXPERIMENTS AND THEORY
Differently from the popular Deep Q-Network (DQN) learning, Alternating Q-learning (AltQ) does not fully fit a target Q-function at each iteration, and is generally known to be unstable and inefficient.
Accelerated Target Updates for Q-learning
This paper studies accelerations in Q-learning algorithms.
