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Found 6 papers, 3 papers with code
Actor-Critic with variable time discretization via sustained actions
Reinforcement learning (RL) methods work in discrete time.
Detecting Out-of-distribution Objects Using Neuron Activation Patterns
Object detection is essential to many perception algorithms used in modern robotics applications.
Combating noisy labels in object detection datasets
This approach shows promising potential in rectifying state-of-the-art object detection datasets.
Emergency action termination for immediate reaction in hierarchical reinforcement learning
In this paper, we address this gap in the state-of-the-art approaches and propose a method in which the validity of higher-level actions (thus lower-level goals) is constantly verified at the higher level.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
ACERAC: Efficient reinforcement learning in fine time discretization
It is not feasible because it causes the controlled system to jerk, and does not ensure sufficient exploration since a~single action is not long enough to create a~significant experience that could be translated into policy improvement.
A framework for reinforcement learning with autocorrelated actions
Consequently, an agent learns from experiments that are distributed over time and potentially give better clues to policy improvement.
