Add to ORKGArtificial intelligence or machine learning techniques are currently being widely applied for solving problems within the field of data analytics. This work presents and demonstrates the use of a new machine learning algorithm for solving semi-Markov decision processes (SMDPs). SMDPs are encountered in the domain of Reinforcement Learning to solve control problems in discrete-event systems. The new algorithm developed here is called iSMART, an acronym for imaging Semi-Markov Average Reward Technique. The algorithm uses a constant exploration rate, unlike its precursor R-SMART, which required exploration decay. The major difference between R-SMART and iSMART is that the latter uses, in addition to the regular iterates of R-SMART, a set of so...
A large class of problems of sequential decision making under uncertainty, of which the underlying p...
Eco-driving involves adaptively changing the speed of the vehicle to ensure minimal fuel consumption...
Reinforcement Learning (RL) is a machine learning paradigm where an agent learns to accomplish seque...
This thesis presents a new actor-critic algorithm from the domain of reinforcement learning to solve...
In the advent of Big Data and Machine Learning, there is a demand for improved decision making in un...
Reinforcement Learning (RL) has achieved tremendous empirical successes in real-world decision-makin...
In this paper, we give a brief review of Markov Decision Processes (MDPs), and how Reinforcement Lea...
Applying the reinforcement learning methodology to domains that involve risky decisions like medicin...
Total Productive Maintenance (TPM) is a critical activity that significantly reduces lead times and ...
A Markov decision process can be parameterized by a transition kernel and a reward function. Both pl...
General purpose intelligent learning agents cycle through (complex,non-MDP) sequences of observatio...
Reinforcement Learning (RL) is the field of research focused on solving sequential decision-making t...
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer S...
Reinforcement Learning (RL) is a subset of machine learning primarily concerned with goal-directed l...
This thesis considers three complications that arise from applying reinforcement learning to a real-...
A large class of problems of sequential decision making under uncertainty, of which the underlying p...
Eco-driving involves adaptively changing the speed of the vehicle to ensure minimal fuel consumption...
Reinforcement Learning (RL) is a machine learning paradigm where an agent learns to accomplish seque...
This thesis presents a new actor-critic algorithm from the domain of reinforcement learning to solve...
In the advent of Big Data and Machine Learning, there is a demand for improved decision making in un...
Reinforcement Learning (RL) has achieved tremendous empirical successes in real-world decision-makin...
In this paper, we give a brief review of Markov Decision Processes (MDPs), and how Reinforcement Lea...
Applying the reinforcement learning methodology to domains that involve risky decisions like medicin...
Total Productive Maintenance (TPM) is a critical activity that significantly reduces lead times and ...
A Markov decision process can be parameterized by a transition kernel and a reward function. Both pl...
General purpose intelligent learning agents cycle through (complex,non-MDP) sequences of observatio...
Reinforcement Learning (RL) is the field of research focused on solving sequential decision-making t...
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer S...
Reinforcement Learning (RL) is a subset of machine learning primarily concerned with goal-directed l...
This thesis considers three complications that arise from applying reinforcement learning to a real-...
A large class of problems of sequential decision making under uncertainty, of which the underlying p...
Eco-driving involves adaptively changing the speed of the vehicle to ensure minimal fuel consumption...
Reinforcement Learning (RL) is a machine learning paradigm where an agent learns to accomplish seque...