arXiv:2405.10369
공식 코드 · Papers with Code
Reinforcement learning
원문 초록
Observing celestial objects and advancing our scientific knowledge about them involves tedious planning, scheduling, data collection and data post-processing. Many of these operational aspects of astronomy are guided and executed by expert astronomers. Reinforcement learning is a mechanism where we (as humans and astronomers) can teach agents of artificial intelligence to perform some of these tedious tasks. In this paper, we will present a state of the art overview of reinforcement learning and how it can benefit astronomy.
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Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing
Autonomous Resource Management in Microservice Systems via Reinforcement Learning
Actor-Critic Deep Reinforcement Learning for Solving Job Shop Scheduling Problems
High-Throughput Distributed Reinforcement Learning via Adaptive Policy Synchronization
Step-wise Policy for Rare-tool Knowledge (SPaRK): Offline RL that Drives Diverse Tool Use in LLMs
Exploring the robustness of TractOracle methods in RL-based tractography
토론
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