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Found 7 papers, 4 papers with code
The SwaNNFlight System: On-the-Fly Sim-to-Real Adaptation via Anchored Learning
And (iii) anchor critics to help stabilize the fine-tuning of agents during sim-to-real transfer, online learning from real data while retaining behavior optimized in simulation.
Honey, I Shrunk The Actor: A Case Study on Preserving Performance with Smaller Actors in Actor-Critic RL
Actors and critics in actor-critic reinforcement learning algorithms are functionally separate, yet they often use the same network architectures.
Regularizing Action Policies for Smooth Control with Reinforcement Learning
A critical problem with the practical utility of controllers trained with deep Reinforcement Learning (RL) is the notable lack of smoothness in the actions learned by the RL policies.
Observing the Invisible: Live Cache Inspection for High-Performance Embedded Systems
The vast majority of high-performance embedded systems implement multi-level CPU cache hierarchies.
Other Computer Science
Reinforcement Learning for UA V Attitude Control
Abstract—Autopilot systems are typically composed of an “inner loop” providing stability and control, while an “outer loop” is responsible for mission-level objectives, e. g. way-point navigation.
Reinforcement Learning for UAV Attitude Control
Autopilot systems are typically composed of an "inner loop" providing stability and control, while an "outer loop" is responsible for mission-level objectives, e. g. way-point navigation.
Robotics
Deterministic Memory Abstraction and Supporting Multicore System Architecture
In this paper, we make a case that a fundamental problem that prevents efficient and predictable real-time computing on multicore is the lack of a proper memory abstraction to express memory criticality, which cuts across various layers of the system: the application, OS, and hardware.
Hardware Architecture Operating Systems Performance
