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Found 9 papers, 0 papers with code
Dual Ensemble Kalman Filter for Stochastic Optimal Control
In this paper, stochastic optimal control problems in continuous time and space are considered.
Controlled Interacting Particle Algorithms for Simulation-based Reinforcement Learning
This paper is concerned with optimal control problems for control systems in continuous time, and interacting particle system methods designed to construct approximate control solutions.
Convex Q-Learning, Part 1: Deterministic Optimal Control
It is shown that in fact the algorithms are very different: while convex Q-learning solves a convex program that approximates the Bellman equation, theory for DQN is no stronger than for Watkins' algorithm with function approximation: (a) it is shown that both seek solutions to the same fixed point equation, and (b) the ODE approximations for the two algorithms coincide, and little is known about the stability of this ODE.
Q-learning with Uniformly Bounded Variance: Large Discounting is Not a Barrier to Fast Learning
Sample complexity bounds are a common performance metric in the Reinforcement Learning literature.
Zap Q-Learning With Nonlinear Function Approximation
Based on multiple experiments with a range of neural network sizes, it is found that the new algorithms converge quickly and are robust to choice of function approximation architecture.
Zap Q-Learning for Optimal Stopping Time Problems
The objective in this paper is to obtain fast converging reinforcement learning algorithms to approximate solutions to the problem of discounted cost optimal stopping in an irreducible, uniformly ergodic Markov chain, evolving on a compact subset of $\mathbb{R}^n$.
Differential Temporal Difference Learning
Value functions derived from Markov decision processes arise as a central component of algorithms as well as performance metrics in many statistics and engineering applications of machine learning techniques.
Fastest Convergence for Q-learning
The Zap Q-learning algorithm introduced in this paper is an improvement of Watkins' original algorithm and recent competitors in several respects.
Differential TD Learning for Value Function Approximation
The algorithm introduced in this paper is intended to resolve two well-known problems with this approach: In the discounted-cost setting, the variance of the algorithm diverges as the discount factor approaches unity.
