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Found 5 papers, 4 papers with code
Computer-Vision-Enabled Worker Video Analysis for Motion Amount Quantification
Using joint position data from posture estimation, the framework employs Hotelling's T$^2$ statistic to quantify and monitor motion amounts, integrating computer vision tools to address challenges in automated worker training and enhance exploratory research in this field.
Scalable Reinforcement Learning Policies for Multi-Agent Control
Our method can handle an arbitrary number of pursuers and targets; we show results for tasks consisting up to 1000 pursuers tracking 1000 targets.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Learning to Track Dynamic Targets in Partially Known Environments
In particular, we introduce Active Tracking Target Network (ATTN), a unified RL policy that is capable of solving major sub-tasks of active target tracking -- in-sight tracking, navigation, and exploration.
Learning Q-network for Active Information Acquisition
In this paper, we propose a novel Reinforcement Learning approach for solving the Active Information Acquisition problem, which requires an agent to choose a sequence of actions in order to acquire information about a process of interest using on-board sensors.
Assumed Density Filtering Q-learning
We formulate an efficient closed-form solution for the value update by approximately estimating analytic parameters of the posterior of the Q-beliefs.
