Add to ORKGMost of existing adaptive control schemes are designed to minimize error between plant state and goal state despite the fact that executing actions that are predicted to result in smaller errors only can mislead to non-goal states. We develop an adaptive control scheme that involves manipulating a controller of a general type to improve its performance as measured by an evaluation function. The developed method is closely related to a theory of Reinforcement Learning (RL) but imposes a practical assumption made for faster learning. We assume that a value function of RL can be approximated by a function of Euclidean distance from a goal state and an action executed at the state. And, we propose to us...
The reinforcement learning (RL) framework enables to construct controllers that try to find find an ...
Adaptive control is an attractive method to solve control problems since the tedious task of identif...
Much recent work in reinforcement learning and stochastic optimal control has focused on algorithms ...
Most of existing adaptive control schemes are designed to minimize error between plant state an...
dynamic programming using function approximators Preface Control systems are making a tremendous imp...
A reinforcement learning (RL) control policy could fail in a new/perturbed environment that is diffe...
This paper reviews an existing algorithm for adaptive control based on explicit criterion maximizati...
Summarization: Reinforcement Learning methods for controlling stochastic processes typically assume ...
The framework of dynamic programming (DP) and reinforcement learning (RL) can be used to express imp...
In this work, we study model-based reinforcement learning (RL) in unknown stabilizable linear dynami...
Abstract—Reinforcement learning and policy search methods can in principle solve a wide range of con...
It is known that existing policy gradient methods (such as vanilla policy gradient, PPO, A2C) may su...
I herby declare that I am the sole author of this thesis. This is a true copy of the thesis, includi...
We consider the policy search approach to reinforcement learning. We show that if a “baseline distri...
Reinforcement learning is challenging if state and action spaces are continuous. The discretization ...
The reinforcement learning (RL) framework enables to construct controllers that try to find find an ...
Adaptive control is an attractive method to solve control problems since the tedious task of identif...
Much recent work in reinforcement learning and stochastic optimal control has focused on algorithms ...
Most of existing adaptive control schemes are designed to minimize error between plant state an...
dynamic programming using function approximators Preface Control systems are making a tremendous imp...
A reinforcement learning (RL) control policy could fail in a new/perturbed environment that is diffe...
This paper reviews an existing algorithm for adaptive control based on explicit criterion maximizati...
Summarization: Reinforcement Learning methods for controlling stochastic processes typically assume ...
The framework of dynamic programming (DP) and reinforcement learning (RL) can be used to express imp...
In this work, we study model-based reinforcement learning (RL) in unknown stabilizable linear dynami...
Abstract—Reinforcement learning and policy search methods can in principle solve a wide range of con...
It is known that existing policy gradient methods (such as vanilla policy gradient, PPO, A2C) may su...
I herby declare that I am the sole author of this thesis. This is a true copy of the thesis, includi...
We consider the policy search approach to reinforcement learning. We show that if a “baseline distri...
Reinforcement learning is challenging if state and action spaces are continuous. The discretization ...
The reinforcement learning (RL) framework enables to construct controllers that try to find find an ...
Adaptive control is an attractive method to solve control problems since the tedious task of identif...
Much recent work in reinforcement learning and stochastic optimal control has focused on algorithms ...