Tuning the Weights: The Impact of Initial Matrix Configurations on Successor Features’ Learning Efficacy
The focus of this study is to investigate the impact of different initialization strategies for the weight matrix of Successor Features (SF) on the learning efficiency and convergence in Reinforcement Learning (RL) agents. Using a grid-world paradigm, we compare the performance of RL agents, whose S...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-10-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/20/4212 |