Summary of An Examination Of Offline-trained Encoders in Vision-based Deep Reinforcement Learning For Autonomous Driving, by Shawan Mohammed et al.
An Examination of Offline-Trained Encoders in Vision-Based Deep Reinforcement Learning for Autonomous Driving
by Shawan Mohammed, Alp Argun, Nicolas Bonnotte, Gerd Ascheid
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach is proposed to address the challenges faced by Deep Reinforcement Learning (DRL) in complex Partially Observable Markov Decision Processes (POMDP), specifically autonomous driving. The partial observability issue is mitigated by augmenting sensor information and data fusion, but this requires a more complex perception module that can be trained via RL. However, as the neural network architecture becomes more complex, the effectiveness of the reward function as an error signal diminishes due to noisy, sparse, and delayed supervision. The proposed solution adopts offline-trained encoders for self-supervised learning from large video datasets, followed by DRL training for controlling ego vehicles in the CARLA simulator. This study investigates the impact of different learning schemes on the performance of DRL agents in challenging autonomous driving tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous cars are super smart computers that drive themselves! They use something called Deep Reinforcement Learning (DRL) to make decisions. But, there’s a problem – these computers can’t see everything all at once. This makes it hard for them to decide what to do next. Our team found a way to make the computer see more by combining different types of information. Then, we trained another part of the computer to use this new information to drive safely in a special simulator. We tested our approach and found that it works really well! This is important because it could help make self-driving cars even better. |
Keywords
» Artificial intelligence » Neural network » Reinforcement learning » Self supervised
