Reinforcement learning and optimal adaptive control: An overview and implementation examples
This paper provides an overview of the reinforcement learning and optimal adaptive control
literature and its application to robotics. Reinforcement learning is bridging the gap between …
literature and its application to robotics. Reinforcement learning is bridging the gap between …
Reinforcement learning with guarantees: a review
Reinforcement learning is concerned with a generic concept of an agent acting in an
environment. From the control theory standpoint, reinforcement learning may be considered …
environment. From the control theory standpoint, reinforcement learning may be considered …
Experience replay for real-time reinforcement learning control
Reinforcement-learning (RL) algorithms can automatically learn optimal control strategies
for nonlinear, possibly stochastic systems. A promising approach for RL control is …
for nonlinear, possibly stochastic systems. A promising approach for RL control is …
Approximate policy iteration: A survey and some new methods
DP Bertsekas - Journal of Control Theory and Applications, 2011 - Springer
We consider the classical policy iteration method of dynamic programming (DP), where
approximations and simulation are used to deal with the curse of dimensionality. We survey …
approximations and simulation are used to deal with the curse of dimensionality. We survey …
Real-time measurement-driven reinforcement learning control approach for uncertain nonlinear systems
The paper introduces an interactive machine learning mechanism to process the
measurements of an uncertain, nonlinear dynamic process and hence advise an actuation …
measurements of an uncertain, nonlinear dynamic process and hence advise an actuation …
A data-driven reinforcement learning solution framework for optimal and adaptive personalization of a hip exoskeleton
Robotic exoskeletons are exciting technologies for augmenting human mobility. However,
designing such a device for seamless integration with the human user and to assist human …
designing such a device for seamless integration with the human user and to assist human …
Approximate reinforcement learning: An overview
Reinforcement learning (RL) allows agents to learn how to optimally interact with complex
environments. Fueled by recent advances in approximation-based algorithms, RL has …
environments. Fueled by recent advances in approximation-based algorithms, RL has …
Overcoming the challenges in cost estimation for distributed software projects
N Ramasubbu, RK Balan - 2012 34th international conference …, 2012 - ieeexplore.ieee.org
We describe how we studied, in-situ, the operational processes of three large high process
maturity distributed software development companies and discovered three common …
maturity distributed software development companies and discovered three common …
Pneumatic artificial muscle-driven robot control using local update reinforcement learning
In this study, a new value function based Reinforcement learning (RL) algorithm, Local
Update Dynamic Policy Programming (LUDPP), is proposed. It exploits the nature of smooth …
Update Dynamic Policy Programming (LUDPP), is proposed. It exploits the nature of smooth …
Q-learning and enhanced policy iteration in discounted dynamic programming
DP Bertsekas, H Yu - Mathematics of Operations Research, 2012 - pubsonline.informs.org
We consider the classical finite-state discounted Markovian decision problem, and we
introduce a new policy iteration-like algorithm for finding the optimal state costs or Q-factors …
introduce a new policy iteration-like algorithm for finding the optimal state costs or Q-factors …