Reinforcement learning in Markovian and non-Markovian environments
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Reinforcement learning for robots using neural networks
Reinforcement learning for robots using neural networks
Reinforcement learning of non-Markov decision processes
Artificial Intelligence - Special volume on computational research on interaction and agency, part 2
Neural Computation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Learning precise timing with lstm recurrent networks
The Journal of Machine Learning Research
Learning to Forget: Continual Prediction with LSTM
Neural Computation
Training Recurrent Networks by Evolino
Neural Computation
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Learning resources in federated environments: a broken link checker based on URL similarity
International Journal of Metadata, Semantics and Ontologies
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Reinforcement learning (RL) has been widely used to solve problems with a little feedback from environment. Q learning can solve Markov decision processes (MDPs) quite well. For partially observable Markov decision processes (POMDPs), a recurrent neural network (RNN) can be used to approximate Q values. However, learning time for these problems is typically very long. We present a new combination of RL and RNN to find a good policy for POMDPs in a shorter learning time. This method contains two phases: firstly, state space is divided into two groups (fully observable state group and hidden state group); secondly, a Q value table is used to store values of fully observable states and an RNN is used to approximate values for hidden states. Results of experiments in two grid world problems show that the proposed method enables an agent to acquire a policy with better learning performance compared to the method using only a RNN.