Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a state estimation approach for reinforce- ment learning (RL) of a partially observable Markov decision process. It is based on a special recurrent neural network architecture, the Markov deci- sion process extraction network with shortcuts (MPEN-S). In contrast to previous work regarding this topic,  address the problem of long-term de- pendencies, which cause major problems in many real-world applications. The architecture is designed to model the reward-relevant dynamics of an environment and is capable to condense large sets of continuous observ- ables to a compact Markovian state representation. The resulting estimate can be used as input for RL methods that assume the underlying system to be a Markov decision process. Although the approach was developed with RL in mind, it is also useful for general prediction tasks.
['Siegmund Duell', 'Lina Weichbrodt', 'Alexander Hans', 'Steffen Udluft']
I am writing a research paper on a idea Recurrent Neural State Estimation in Domains with Long-Term Dependencies
[{"from": "human", "value": "I am writing a research paper on a idea Recurrent Neural State Estimation in Domains with Long-Term Dependencies"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a state estimation approach for reinforce- ment learning (RL) of a partially observable Markov decision process. It is based on a special recurrent neural network architecture, the Markov deci- sion process extraction network with shortcuts (MPEN-S). In contrast to previous work regarding this topic,  address the problem of long-term de- pendencies, which cause major problems in many real-world applications. The architecture is designed to model the reward-relevant dynamics of an environment and is capable to condense large sets of continuous observ- ables to a compact Markovian state representation. The resulting estimate can be used as input for RL methods that assume the underlying system to be a Markov decision process. Although the approach was developed with RL in mind, it is also useful for general prediction tasks."}]