Add to ORKGThis study is concerned with finite Markov decision processes (MDPs) whose state are exactly observable but its transition matrix is unknown. We develop a learning algorithm of the reward-penalty type for the communicating case of multi-chain MDPs. An adaptively optimal policy and an asymptotic sequence of adaptive policies with nearly optimal properties are constructed under the average expected reward criterion. Also, a numerical experiment is given to show the practical effectiveness of the algorithm
In this paper we consider the problem of reinforcement learning in a dynamically changing environmen...
Solving Markov decision processes (MDPs) efficiently is challenging in many cases, for example, when...
This chapter presents an overview of simulation-based techniques useful for solving Markov decision ...
We consider the problem of "optimal learning" for Markov decision processes with uncertain...
We introduce a class of Markov decision problems (MDPs) which greatly simplify Reinforcement Learnin...
Learning in Partially Observable Markov Decision process (POMDP) is motivated by the essential need ...
Algorithms for learning the optimal policy of a Markov decision process (MDP) based on simulated tra...
Increasing attention has been paid to reinforcement learning algorithms in recent years, partly due ...
<p>This dissertation describes sequential decision making problems in non-stationary environments. O...
In this paper, a mapping is developed between the ‘multichain’ and ‘unchain’ linear programs for ave...
We study model-based reinforcement learning (RL) for episodic Markov decision processes (MDP) whose ...
A Markov decision process (MDP) relies on the notions of state, describing the current situation of ...
The running time of the classical algorithms of the Markov Decision Process (MDP) typically grows li...
We introduce a class of MPDs which greatly simplify Reinforcement Learning. They have discrete state...
We present a class of metrics, defined on the state space of a finite Markov decision process (MDP)...
In this paper we consider the problem of reinforcement learning in a dynamically changing environmen...
Solving Markov decision processes (MDPs) efficiently is challenging in many cases, for example, when...
This chapter presents an overview of simulation-based techniques useful for solving Markov decision ...
We consider the problem of "optimal learning" for Markov decision processes with uncertain...
We introduce a class of Markov decision problems (MDPs) which greatly simplify Reinforcement Learnin...
Learning in Partially Observable Markov Decision process (POMDP) is motivated by the essential need ...
Algorithms for learning the optimal policy of a Markov decision process (MDP) based on simulated tra...
Increasing attention has been paid to reinforcement learning algorithms in recent years, partly due ...
<p>This dissertation describes sequential decision making problems in non-stationary environments. O...
In this paper, a mapping is developed between the ‘multichain’ and ‘unchain’ linear programs for ave...
We study model-based reinforcement learning (RL) for episodic Markov decision processes (MDP) whose ...
A Markov decision process (MDP) relies on the notions of state, describing the current situation of ...
The running time of the classical algorithms of the Markov Decision Process (MDP) typically grows li...
We introduce a class of MPDs which greatly simplify Reinforcement Learning. They have discrete state...
We present a class of metrics, defined on the state space of a finite Markov decision process (MDP)...
In this paper we consider the problem of reinforcement learning in a dynamically changing environmen...
Solving Markov decision processes (MDPs) efficiently is challenging in many cases, for example, when...
This chapter presents an overview of simulation-based techniques useful for solving Markov decision ...