Publication
Mutual information weighing for probabilistic movement primitives
Conference Article
Conference
Catalan Conference on Artificial Intelligence (CCIA)
Edition
24th
Pages
365-368
Doc link
https://doi.org/10.3233/FAIA220359
File
Abstract
Reinforcement Learning (RL) of trajectory data has been used in several fields, and it is of relevance in robot motion learning, in which sampled trajectories are run and their outcome is evaluated with a reward value. The responsibility on the performance of a task can be associated to the trajectory as a whole, or distributed throughout its points (timesteps). In this work, we present a novel method for attributing the responsibility of the rewards to each time-step separately by using Mutual Information (MI) to bias the model fitting of a trajectory.
Categories
artificial intelligence, robots, stochastic programming.
Scientific reference
A. Colomé and C. Torras. Mutual information weighing for probabilistic movement primitives, 24th Catalan Conference on Artificial Intelligence, 2022, Sitges, in Artificial Intelligence Research and Development, Vol 356 of Frontiers in Artificial Intelligence and Applications, pp. 365-368, IOS Press.
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