Add to ORKGApproximate dynamic programming (ADP) is to com-pute near-optimal solutions to Markov decision problems (MDPs) with large or continuous spaces. In recent years, the research works on ADP have been brought together with the reinforcement learning (RL) community [1-4]. RL is a machine learning framework for solving sequen-tial decision making problems that can also be modeled as the MDP formalism. The common objective of RL and ADP is to develop efficient algorithms for sequential decision making under uncertain complex conditions. Therefore, there are many potential applications of RL and ADP in real-world problems such as autonomous robots, intelligent control, resource allocation, network routing, etc. This special section of JILSA focuses...
The semi-Markov decision process (SMDP) is a variant of the Markov decision process (MOP). This diss...
The semi-Markov decision process (SMDP) is a variant of the Markov decision process (MOP). This diss...
Two common approaches to sequential decision-making are AI planning (AIP) and reinforcement learning...
In the last few years, Reinforcement Learning (RL), also called adaptive (or approximate) dynamic pr...
The sixteen papers in this special section focus on deep reinforcement learning and adaptive dynamic...
The sixteen papers in this special section focus on deep reinforcement learning and adaptive dynamic...
The sixteen papers in this special section focus on deep reinforcement learning and adaptive dynamic...
The sixteen papers in this special section focus on deep reinforcement learning and adaptive dynamic...
In control systems theory, the Markov decision process (MDP) is a widely used optimization model inv...
Reinforcement learning algorithms hold promise in many complex domains, such as resource management ...
The application of reinforcement learning to problems with continuous domains requires rep-resenting...
The framework of dynamic programming (DP) and reinforcement learning (RL) can be used to express imp...
This is an updated version of the research-oriented Chapter 6 on Approximate Dynamic Programming. It...
International audienceIn any complex or large scale sequential decision making problem, there is a c...
The two-part series of papers provides a survey on recent advances in Deep Reinforcement Learning (D...
The semi-Markov decision process (SMDP) is a variant of the Markov decision process (MOP). This diss...
The semi-Markov decision process (SMDP) is a variant of the Markov decision process (MOP). This diss...
Two common approaches to sequential decision-making are AI planning (AIP) and reinforcement learning...
In the last few years, Reinforcement Learning (RL), also called adaptive (or approximate) dynamic pr...
The sixteen papers in this special section focus on deep reinforcement learning and adaptive dynamic...
The sixteen papers in this special section focus on deep reinforcement learning and adaptive dynamic...
The sixteen papers in this special section focus on deep reinforcement learning and adaptive dynamic...
The sixteen papers in this special section focus on deep reinforcement learning and adaptive dynamic...
In control systems theory, the Markov decision process (MDP) is a widely used optimization model inv...
Reinforcement learning algorithms hold promise in many complex domains, such as resource management ...
The application of reinforcement learning to problems with continuous domains requires rep-resenting...
The framework of dynamic programming (DP) and reinforcement learning (RL) can be used to express imp...
This is an updated version of the research-oriented Chapter 6 on Approximate Dynamic Programming. It...
International audienceIn any complex or large scale sequential decision making problem, there is a c...
The two-part series of papers provides a survey on recent advances in Deep Reinforcement Learning (D...
The semi-Markov decision process (SMDP) is a variant of the Markov decision process (MOP). This diss...
The semi-Markov decision process (SMDP) is a variant of the Markov decision process (MOP). This diss...
Two common approaches to sequential decision-making are AI planning (AIP) and reinforcement learning...