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Deep Deterministic Path Following

arXiv 2104.06014 ↗First posted 13 Apr 2021cs.RO cs.LGcited by 0

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Abstract (arXiv)

This paper deploys the Deep Deterministic Policy Gradient (DDPG) algorithm for longitudinal and lateral control of a simulated car to solve a path following task. The DDPG agent was implemented using PyTorch and trained and evaluated on a custom kinematic bicycle environment created in Python. The performance was evaluated by measuring cross-track error and velocity error, relative to a reference path. Results show how the agent can learn a policy allowing for small cross-track error, as well as adapting the acceleration to minimize the velocity error.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 8 references that are in the map. First posted on arXiv 13 Apr 2021.

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3 of 8 references.

References · 3

  1. 2015Continuous control with deep reinforcement learningcited by 3,407
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  3. 2018Coupled Longitudinal and Lateral Control of a Vehicle using Deep Learningcited by 1

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