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Found 8 papers, 0 papers with code
Finite-Time Error Analysis of Online Model-Based Q-Learning with a Relaxed Sampling Model
Reinforcement learning has witnessed significant advancements, particularly with the emergence of model-based approaches.
A primal-dual perspective for distributed TD-learning
The goal of this paper is to investigate distributed temporal difference (TD) learning for a networked multi-agent Markov decision process.
Continuous-Time Distributed Dynamic Programming for Networked Multi-Agent Markov Decision Processes
The main goal of this paper is to investigate continuous-time distributed dynamic programming (DP) algorithms for networked multi-agent Markov decision problems (MAMDPs).
Temporal Difference Learning with Experience Replay
Temporal-difference (TD) learning is widely regarded as one of the most popular algorithms in reinforcement learning (RL).
Backstepping Temporal Difference Learning
Off-policy learning ability is an important feature of reinforcement learning (RL) for practical applications.
Finite-Time Analysis of Asynchronous Q-learning under Diminishing Step-Size from Control-Theoretic View
Q-learning has long been one of the most popular reinforcement learning algorithms, and theoretical analysis of Q-learning has been an active research topic for decades.
Regularized Q-learning
This paper develops a new Q-learning algorithm that converges when linear function approximation is used.
New Versions of Gradient Temporal Difference Learning
Sutton, Szepesv\'{a}ri and Maei introduced the first gradient temporal-difference (GTD) learning algorithms compatible with both linear function approximation and off-policy training.
