Dynamic programming: deterministic and stochastic models
Dynamic programming: deterministic and stochastic models
A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Kalman filtering: theory and practice
Kalman filtering: theory and practice
Improved switching among temporally abstract actions
Proceedings of the 1998 conference on Advances in neural information processing systems II
Programming backgammon using self-teaching neural nets
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Learning from Scarce Experience
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Learning Policies with External Memory
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Memory Approaches to Reinforcement Learning in Non-Markovian Domains
Memory Approaches to Reinforcement Learning in Non-Markovian Domains
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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A real world environment is often partially observable by the agents either because of noisy sensors or incomplete perception. Autonomous strategy planning under uncertainty has two major challenges. First, autonomous segmentation of the state space for a given task; Second, emerging complex behaviors that deal with each state segment. This paper suggests a new approach that handles both by utilizing combination of various techniques, namely ARKAQ-Learning (ART 2-A networks augmented with Kalman Filters and Q-Learning). The algorithm is an online algorithm and it has low space and computational complexity. The algorithm was run for some well known partially observable Markov decision process problems. World Model Generator could reveal the hidden states, mapping non-Markovian model to Markovian internal state space. Policy Generator could build the optimal policy on the internal Markovian state model.