HAS-RL: A Hierarchical Approximate Scheme Optimized With Reinforcement Learning for NoC-Based NN Accelerators

Network-on-Chip (NoC) is a scalable on-chip communication architecture for the NN accelerator, but with the increase in the number of nodes, the communication delay becomes higher. Applications such as machine learning have a certain resilience to noisy/erroneous transmitted data. Therefore, approximate communication becomes a promising solution...

Online robot guidance and navigation in non-stationary environment with hybrid Hierarchical Reinforcement Learning

Hierarchical Reinforcement Learning (HRL) provides an option to solve complex guidance and navigation problems with high-dimensional spaces, multiple objectives, and a large number of states and actions. The current HRL methods often use the same or similar reinforcement learning methods within one application so that multiple objectives can...

Efficient Online Globalized Dual Heuristic Programming With an Associated Dual Network

Globalized dual heuristic programming (GDHP) is the most comprehensive adaptive critic design, which employs its critic to minimize the error with respect to both the cost-to-go and its derivatives simultaneously. Its implementation, however, confronts a dilemma of either introducing more computational load by explicitly calculating the...

Online reinforcement learning control for aerospace systems

Reinforcement Learning (RL) methods are relatively new in the field of aerospace guidance, navigation, and control. This dissertation aims to exploit RL methods to improve the autonomy and online learning of aerospace systems with respect to the a priori unknown system and environment, dynamical uncertainties, and partial observability. In the...