Add to ORKGEffective decomposition and abstraction has been shown to improve the performance of Reinforcement Learning. An agent can use the clues from the environment to either partition the problem into sub-problems or get informed about its progress in a given task. In a fully observable environment such clues may come from subgoals while in a partially observable environment they may be provided by unique experiences. The contribution of this thesis is two fold; first improvements over automatic subgoal identification and option generation in fully observable environments is proposed, then an automatic landmark identification and an anchor based guiding mechanism in partially observable environments is introduced. Moreover, for both type of proble...
* This research was partially supported by the Latvian Science Foundation under grant No.02-86d.Effi...
Common approaches to Reinforcement Learning (RL) are seriously challenged by large-scale application...
Temporal abstraction for reinforcement learning (RL) aims to decrease learning time by making use of...
This thesis focuses on Reinforcement Learning (RL) which considers an agent that makes sequen- tial ...
Subgoal discovery in reinforcement learning is an effective way of partitioning a problem domain wit...
Reinforcement learning is the problem faced by an agent that must learn behavior through trial-and-e...
textReinforcement Learning (RL) offers a promising approach towards achieving the dream of autonomou...
Applying reinforcement learning techniques to real-world problems as well as long standing challenge...
Reinforcement learning has long been advertised as the one with the capability to intelligently mimi...
Reinforcement learning addresses the problem of learning to select actions in order to maximize an a...
Reinforcement learning defines a prominent family of unsupervised machine learning methods in autono...
Reinforcement learning provides a means for autonomous agents to improve their action selection stra...
People are efficient when they make decisions under uncertainty, even when their decisions have long...
Reinforcement learning involves the study of how to solve sequential decision-making problems using ...
Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncerta...
* This research was partially supported by the Latvian Science Foundation under grant No.02-86d.Effi...
Common approaches to Reinforcement Learning (RL) are seriously challenged by large-scale application...
Temporal abstraction for reinforcement learning (RL) aims to decrease learning time by making use of...
This thesis focuses on Reinforcement Learning (RL) which considers an agent that makes sequen- tial ...
Subgoal discovery in reinforcement learning is an effective way of partitioning a problem domain wit...
Reinforcement learning is the problem faced by an agent that must learn behavior through trial-and-e...
textReinforcement Learning (RL) offers a promising approach towards achieving the dream of autonomou...
Applying reinforcement learning techniques to real-world problems as well as long standing challenge...
Reinforcement learning has long been advertised as the one with the capability to intelligently mimi...
Reinforcement learning addresses the problem of learning to select actions in order to maximize an a...
Reinforcement learning defines a prominent family of unsupervised machine learning methods in autono...
Reinforcement learning provides a means for autonomous agents to improve their action selection stra...
People are efficient when they make decisions under uncertainty, even when their decisions have long...
Reinforcement learning involves the study of how to solve sequential decision-making problems using ...
Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncerta...
* This research was partially supported by the Latvian Science Foundation under grant No.02-86d.Effi...
Common approaches to Reinforcement Learning (RL) are seriously challenged by large-scale application...
Temporal abstraction for reinforcement learning (RL) aims to decrease learning time by making use of...