A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2024; you can also visit the original URL.
The file type is application/pdf
.
Imaginary Hindsight Experience Replay: Curious Model-based Learning for Sparse Reward Tasks
[article]
2023
arXiv
pre-print
Model-based reinforcement learning is a promising learning strategy for practical robotic applications due to its improved data-efficiency versus model-free counterparts. However, current state-of-the-art model-based methods rely on shaped reward signals, which can be difficult to design and implement. To remedy this, we propose a simple model-based method tailored for sparse-reward multi-goal tasks that foregoes the need for complicated reward engineering. This approach, termed Imaginary
arXiv:2110.02414v2
fatcat:viad74ufq5gxxop5k3gzlf56ju