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About Integrating Deep Reinforcement Learning With Model-Based Path Planner
This document proposes a hybrid approach that integrates deep reinforcement learning (DRL) with model-based path planners for automated driving. Specifically, it trains a DRL agent to follow waypoints generated by an A* path planner as closely as possible, by penalizing the agent for straying from the waypoints and collisions. The agent learns this policy through interacting with a simulated driving environment. Experimental results show the approach can successfully plan paths and navigate between points in a dynamic urban simulation.
Detailed Information
| Author: | ['Adnan Hossain'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 6 |
| Language: | English |
| Format: | |
| Price: | FREE |
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