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Found 7 papers, 3 papers with code
Subequivariant Reinforcement Learning Framework for Coordinated Motion Control
Effective coordination is crucial for motion control with reinforcement learning, especially as the complexity of agents and their motions increases.
PILLOW: Enhancing Efficient Instruction Fine-tuning via Prompt Matching
Instruction fine-tuning has conventionally been employed to adapt Large Language Models (LLMs) to a variety of tasks.
Gram-based Attentive Neural Ordinary Differential Equations Network for Video Nystagmography Classification
Video nystagmography (VNG) is the diagnostic gold standard of benign paroxysmal positional vertigo (BPPV), which requires medical professionals to examine the direction, frequency, intensity, duration, and variation in the strength of nystagmus on a VNG video.
A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud
Predictive autoscaling (autoscaling with workload forecasting) is an important mechanism that supports autonomous adjustment of computing resources in accordance with fluctuating workload demands in the Cloud.
Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point Processes
We consider a sequential decision making problem where the agent faces the environment characterized by the stochastic discrete events and seeks an optimal intervention policy such that its long-term reward is maximized.
Model Embedding Model-Based Reinforcement Learning
Model-based reinforcement learning (MBRL) has shown its advantages in sample-efficiency over model-free reinforcement learning (MFRL).
Model-based Reinforcement Learning
reinforcement-learning
+1
S2VG: Soft Stochastic Value Gradient method
In this paper, we propose a simple and elegant model-based reinforcement learning algorithm called soft stochastic value gradient method (S2VG).
Model-based Reinforcement Learning
reinforcement-learning
+1
