Add to ORKGAbstract. Reinforcement Learning (RL) is analyzed here as a tool for control system optimization. State and action spaces are assumed to be continuous. Time is assumed to be discrete, yet the discretization may be arbitrarily fine. It is shown here that stationary policies, applied by most RL methods, are improper in control applications, since for fine time discretization they can not assure bounded variance of policy gradient estimators. As a remedy to that difficulty, we propose the use of piecewise non-Markov policies. Policies of this type can be optimized by means of most RL algorithms, namely those based on likelihood ratio.
This paper presents a reinforcement learning framework for continuous-time dynamical systems without...
ENNS best student paper awardInternational audienceReinforcement learning induces non-stationarity a...
ENNS best student paper awardInternational audienceReinforcement learning induces non-stationarity a...
Reinforcement learning is a family of machine learning algorithms, in which the system learns to mak...
The framework of dynamic programming (DP) and reinforcement learning (RL) can be used to express imp...
This paper reviews the current state of the art on reinforcement learning (RL)-based feedback contro...
Reinforcement learning (RL) has emerged as a general-purpose technique for addressing problems invol...
Reinforcement learning is a learning paradigm concerned with learning to control a system so as to m...
Reinforcement learning (RL) methods work in discrete time. In order to apply RL to inherently contin...
Abstract. Markov Decision Process (MDP) has enormous applications in science, engineering, economics...
Dynamic control tasks are good candidates for the application of reinforcement learning techniques. ...
Abstract. Reinforcement learning induces non-stationarity at several levels. Adaptation to non-stati...
Reinforcement learning is a powerful approach for learning control policies that solve sequential de...
Reinforcement learning describes how an agent can learn to act in an unknown environment in order to...
Reinforcement learning describes how an agent can learn to act in an unknown environment in order to...
This paper presents a reinforcement learning framework for continuous-time dynamical systems without...
ENNS best student paper awardInternational audienceReinforcement learning induces non-stationarity a...
ENNS best student paper awardInternational audienceReinforcement learning induces non-stationarity a...
Reinforcement learning is a family of machine learning algorithms, in which the system learns to mak...
The framework of dynamic programming (DP) and reinforcement learning (RL) can be used to express imp...
This paper reviews the current state of the art on reinforcement learning (RL)-based feedback contro...
Reinforcement learning (RL) has emerged as a general-purpose technique for addressing problems invol...
Reinforcement learning is a learning paradigm concerned with learning to control a system so as to m...
Reinforcement learning (RL) methods work in discrete time. In order to apply RL to inherently contin...
Abstract. Markov Decision Process (MDP) has enormous applications in science, engineering, economics...
Dynamic control tasks are good candidates for the application of reinforcement learning techniques. ...
Abstract. Reinforcement learning induces non-stationarity at several levels. Adaptation to non-stati...
Reinforcement learning is a powerful approach for learning control policies that solve sequential de...
Reinforcement learning describes how an agent can learn to act in an unknown environment in order to...
Reinforcement learning describes how an agent can learn to act in an unknown environment in order to...
This paper presents a reinforcement learning framework for continuous-time dynamical systems without...
ENNS best student paper awardInternational audienceReinforcement learning induces non-stationarity a...
ENNS best student paper awardInternational audienceReinforcement learning induces non-stationarity a...