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Found 6 papers, 0 papers with code
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).
History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms
In this paper, we propose a novel scheme, which eliminates backtracking line search but still exploits the information along optimization path by adapting the batch size via history stochastic gradients.
Hybrid Zero Dynamics Inspired Feedback Control Policy Design for 3D Bipedal Locomotion using Reinforcement Learning
This paper presents a novel model-free reinforcement learning (RL) framework to design feedback control policies for 3D bipedal walking.
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.
