Acting optimally in partially observable stochastic domains
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Feature-based methods for large scale dynamic programming
Machine Learning - Special issue on reinforcement 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
Robust non-linear control through neuroevolution
Robust non-linear control through neuroevolution
Self-Optimizing Memory Controllers: A Reinforcement Learning Approach
ISCA '08 Proceedings of the 35th Annual International Symposium on Computer Architecture
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Reinforcement learning with perceptual aliasing: the perceptual distinctions approach
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Learning finite-state controllers for partially observable environments
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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This paper contributes on designing an autonomous system utilizing self-optimizing memory controller for non-Markovian reinforcement tasks. Instead of holistic search for the whole memory contents, the controller adopts associated feature analysis to produce the most likely relevant action from previous experiences. Actor-Critic (AC) learning is used to adaptively tuning the control parameters, while on-line variant of Random Forest (RF) learner is used as memory-capable to approximate the policy of Actor and the value function of Critic. Learning capability is experimentally examined through non-Markovian cart-pole balancing task. The result shows that the proposed controller acquired complex behaviors such as balancing two poles simultaneously and displays long-term planning.