A Multi-Objective Reinforcement Learning Based Controller for Autonomous Navigation in Challenging Environments
In this paper, we introduce a self-trained controller for autonomous navigation in static and dynamic (with moving walls and nets) challenging environments (including trees, nets, windows, and pipe) using deep reinforcement learning, simultaneously trained using multiple rewards. We train our RL alg...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/10/7/500 |