Combined Model for Partially-Observable and Non-Observable Task Switching: Solving Hierarchical Reinforcement Learning Problems Statically and Dynamically with Transfer Learning
An integral function of fully autonomous robots and humans is the ability to focus attention on a few relevant percepts to reach a certain goal while disregarding irrelevant percepts. Humans and animals rely on the interactions between the Pre-Frontal Cortex (PFC) and the Basal Ganglia (BG) to achieve this focus called Working Memory (WM). The Working Memory Toolkit (WMtk) was developed based on a computational neuroscience model of this phenomenon with Temporal Difference (TD) Learning for autonomous systems. Recent adaptations of the toolkit either utilize Abstract Task Representations (ATRs) to solve Non-Observable (NO) tasks or storage of past input features to solve Partially-Observable (PO) tasks, but not both. We propose a new model, PONOWMtk, which combines both approaches, ATRs and input storage, with a static or dynamic number of ATRs. The results of our experiments show that PONOWMtk performs effectively for tasks that exhibit PO, NO, or both properties.
Code (1)
Tasks
Hierarchical Reinforcement LearningReinforcement LearningTransfer LearningSimilar Papers 제목 키워드 기반
Combined Model for Partially-Observable and Non-Observable Task Switching: Solving Hierarchical Reinforcement Learning Problems Statically and Dynamically with Transfer Learning
An integral function of fully autonomous robots and humans is the ability to focus attention on a few relevant percepts to reach a certain goal while disregarding irrelevant percepts. Humans and animals rely on the inter…
Hierarchical Reinforcement LearningReinforcement LearningTransfer LearningSemantic-aware Sampling and Transmission in Energy Harvesting Systems: A POMDP Approach
We address the problem of real-time remote tracking of a partially observable Markov source in an energy harvesting system with an unreliable communication channel. We consider both sampling and transmission costs. Diffe…
Deep Reinforcement LearningAn intelligent tutor for planning in large partially observable environments
AI can not only outperform people in many planning tasks, but it can also teach them how to plan better. A recent and promising approach to improving human decision-making is to create intelligent tutors that utilize AI …
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems
We study Reinforcement Learning for partially observable dynamical systems using function approximation. We propose a new \textit{Partially Observable Bilinear Actor-Critic framework}, that is general enough to include m…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Policy Gradient in Partially Observable Environments: Approximation and Convergence
Policy gradient is a generic and flexible reinforcement learning approach that generally enjoys simplicity in analysis, implementation, and deployment. In the last few decades, this approach has been extensively advanced…
Decision MakingPolicy Gradient MethodsReinforcement Learning