Add to ORKGWe develop a simulation based algorithm for finite horizon Markov decision processes with finite state and finite action space. Illustrative numerical experiments with the proposed algorithm are shown for problems in flow control of communication networks and capacity switching in semiconductor fabrication
In this paper we consider the problem of reinforcement learning in a dynamically changing environmen...
A linear function approximation-based reinforcement learning algorithm is proposed for Markov decisi...
A linear function approximation-based reinforcement learning algorithm is proposed for Markov decisi...
We develop a simulation based algorithm for finite horizon Markov decision processes with finite sta...
We develop a simulation based algorithm for finite horizon Markov decision processes with finite sta...
We develop a simulation based algorithm for finite horizon Markov decision processes with finite sta...
We develop four simulation-based algorithms for finite-horizon Markov decision processes. Two of the...
We develop four simulation-based algorithms for finite-horizon Markov decision processes. Two of the...
This article proposes several two-timescale simulation-based actor-critic algorithms for solution of...
Q-learning is a popular reinforcement learning algorithm. This algorithm has however been studied an...
In Chapter 2, we propose several two-timescale simulation-based actor-critic algorithms for solution...
Increasing attention has been paid to reinforcement learning algorithms in recent years, partly due ...
This chapter presents an overview of simulation-based techniques useful for solving Markov decision ...
In this paper we consider the problem of reinforcement learning in a dynamically changing environmen...
We propose a reinforcement learning (RL) approach to compute the expression of quasi-stationary dist...
In this paper we consider the problem of reinforcement learning in a dynamically changing environmen...
A linear function approximation-based reinforcement learning algorithm is proposed for Markov decisi...
A linear function approximation-based reinforcement learning algorithm is proposed for Markov decisi...
We develop a simulation based algorithm for finite horizon Markov decision processes with finite sta...
We develop a simulation based algorithm for finite horizon Markov decision processes with finite sta...
We develop a simulation based algorithm for finite horizon Markov decision processes with finite sta...
We develop four simulation-based algorithms for finite-horizon Markov decision processes. Two of the...
We develop four simulation-based algorithms for finite-horizon Markov decision processes. Two of the...
This article proposes several two-timescale simulation-based actor-critic algorithms for solution of...
Q-learning is a popular reinforcement learning algorithm. This algorithm has however been studied an...
In Chapter 2, we propose several two-timescale simulation-based actor-critic algorithms for solution...
Increasing attention has been paid to reinforcement learning algorithms in recent years, partly due ...
This chapter presents an overview of simulation-based techniques useful for solving Markov decision ...
In this paper we consider the problem of reinforcement learning in a dynamically changing environmen...
We propose a reinforcement learning (RL) approach to compute the expression of quasi-stationary dist...
In this paper we consider the problem of reinforcement learning in a dynamically changing environmen...
A linear function approximation-based reinforcement learning algorithm is proposed for Markov decisi...
A linear function approximation-based reinforcement learning algorithm is proposed for Markov decisi...