Non-Markovian Reinforcement-Based on Self-Optimizing Memory Controller
This paper contributes on designing robotic self-optimizing memory controller for non-Markovian reinforcement tasks. Rather than holistic search for the whole memory contents the model adopts associated feature analysis to successively memorize a newly event state-action pair as an action of past experience. Actor-Critic learning is used to adaptively tuning the control parameters, while on-line variant of random forests (RF) learner is used as memory-capable to approximate the policy of Actor and the value function of Critic. Learning capability of the proposed model is experimentally examined through non-markovian cart-pole balancing task. The result shows that our self-optimizing memory controller acquired complex behaviors such as balancing two poles simultaneously, displays long-term planning and generalization capacity based on past experiences.