Summary of Tactical Decision Making For Autonomous Trucks by Deep Reinforcement Learning with Total Cost Of Operation Based Reward, By Deepthi Pathare et al.
Tactical Decision Making for Autonomous Trucks by Deep Reinforcement Learning with Total Cost of Operation Based Reward
by Deepthi Pathare, Leo Laine, Morteza Haghir Chehreghani
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 The proposed deep reinforcement learning framework is designed for autonomous trucks, focusing on Adaptive Cruise Control (ACC) and lane change maneuvers in a highway scenario. The framework separates high-level decision-making processes from low-level control actions between the agent and physical models. By optimizing performance using a realistic multi-objective reward function based on Total Cost of Operation (TCOP), the authors demonstrate the benefits of different approaches, including adding weights to reward components, normalizing them, and using curriculum learning techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed an AI system for autonomous trucks, helping them make smart decisions while driving. They tested this system in a simulated highway scenario, showing that it can improve performance by making better choices about speed and lane changes. The team used a special type of reward function to guide the AI’s learning process. |
Keywords
* Artificial intelligence * Curriculum learning * Reinforcement learning
