Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recent decision-theoric planning algorithms are able to find optimal solutions in large problems, using Factored Markov Decision Processes (FMDPs). Hover, these algorithms need a perfect knowledge of the structure of the problem. ,  propose SDYNA, a general framework for addressing large reinforcement learning problems by trial-and-error and with no initial knowledge of their structure. SDYNA integrates incremental planning algorithms based on FMDPs with supervised learning techniques building structured representations of the problem.  describe SPITI, an instantiation of SDYNA, that uses incremental decision tree induction to learn the structure of a problem combined with an incremental version of the  Structured Value Iteration  algorithm.  show that SPITI can build a factored representation of a reinforcement learning problem and may improve the policy faster than tabular reinforcement learning algorithms by exploiting the generalization property of decision tree induction algorithms.
['Thomas Degris', 'Olivier Sigaud', 'Pierre-Henri Wuillemin']
I am writing a research paper on a idea Learning the structure of Factored Markov Decision Processes in reinforcement learning problems
[{"from": "human", "value": "I am writing a research paper on a idea Learning the structure of Factored Markov Decision Processes in reinforcement learning problems"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recent decision-theoric planning algorithms are able to find optimal solutions in large problems, using Factored Markov Decision Processes (FMDPs). Hover, these algorithms need a perfect knowledge of the structure of the problem. ,  propose SDYNA, a general framework for addressing large reinforcement learning problems by trial-and-error and with no initial knowledge of their structure. SDYNA integrates incremental planning algorithms based on FMDPs with supervised learning techniques building structured representations of the problem.  describe SPITI, an instantiation of SDYNA, that uses incremental decision tree induction to learn the structure of a problem combined with an incremental version of the  Structured Value Iteration  algorithm.  show that SPITI can build a factored representation of a reinforcement learning problem and may improve the policy faster than tabular reinforcement learning algorithms by exploiting the generalization property of decision tree induction algorithms."}]