Add to ORKGRecent research indicates that perturbation analysis (PA), Markov decision process (MDP). and reinforcement learning (RL) are closely related. Policy iteration in MDP can be viewed as a direct consequence of performance sensitivity analysis. Both sensitivity analysis and policy iteration depend on the concept of performance potentials. RL provides efficient on-line algorithms for estimationg potentials and other related quantities such as the Q-factors. The sensitivity point of view brings in some new insight to the area of learning and optimization. In particular, the gradient based optimization can be applied to parameterized systems without suffering the "curse of dimensionality", and can be applied to systems with correlated actions, et...
We study the structure of sample paths of Markov systems by using performance potentials as the fund...
We introduce a sensitivity-based view to the area of learning and optimization of stochastic dynamic...
In the last few years, Reinforcement Learning (RL), also called adaptive (or approximate) dynamic pr...
The goals of perturbation analysis (PA), Markov decision processes (MDPs), and reinforcement learnin...
Recent research indicates that perturbation analysis (PA), Markov decision processes (MDP), and rein...
Learning and optimization of stochastic systems is a multi-disciplinary area that attracts wide atte...
Policy gradient methods are a type of reinforcement learning techniques that rely upon optimizing pa...
This note presents a (new) basic formula for sample-path-based estimates for performance gradients f...
Reinforcement learning is often done using parameterized function approximators to store value funct...
A simple learning rule is derived, the VAPS algorithm, which can be instantiated to generate a wide ...
A policy gradient method is a reinforcement learning approach that directly optimizes a parametrized...
AbstractWe model reinforcement learning as the problem of learning to control a partially observable...
Policy gradient (PG) reinforcement learning algorithms have strong (local) con-vergence guarantees, ...
Reinforcement learning is a family of machine learning algorithms, in which the system learns to mak...
Function approximation is essential to reinforcement learning, but the standard approach of approxi...
We study the structure of sample paths of Markov systems by using performance potentials as the fund...
We introduce a sensitivity-based view to the area of learning and optimization of stochastic dynamic...
In the last few years, Reinforcement Learning (RL), also called adaptive (or approximate) dynamic pr...
The goals of perturbation analysis (PA), Markov decision processes (MDPs), and reinforcement learnin...
Recent research indicates that perturbation analysis (PA), Markov decision processes (MDP), and rein...
Learning and optimization of stochastic systems is a multi-disciplinary area that attracts wide atte...
Policy gradient methods are a type of reinforcement learning techniques that rely upon optimizing pa...
This note presents a (new) basic formula for sample-path-based estimates for performance gradients f...
Reinforcement learning is often done using parameterized function approximators to store value funct...
A simple learning rule is derived, the VAPS algorithm, which can be instantiated to generate a wide ...
A policy gradient method is a reinforcement learning approach that directly optimizes a parametrized...
AbstractWe model reinforcement learning as the problem of learning to control a partially observable...
Policy gradient (PG) reinforcement learning algorithms have strong (local) con-vergence guarantees, ...
Reinforcement learning is a family of machine learning algorithms, in which the system learns to mak...
Function approximation is essential to reinforcement learning, but the standard approach of approxi...
We study the structure of sample paths of Markov systems by using performance potentials as the fund...
We introduce a sensitivity-based view to the area of learning and optimization of stochastic dynamic...
In the last few years, Reinforcement Learning (RL), also called adaptive (or approximate) dynamic pr...