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Autonomous Navigation of Unmanned Vehicle Through Deep Reinforcement Learning

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arxiv 2407.18962 v1 pith:V2YFMSOP submitted 2024-07-18 cs.RO cs.LG

Letian Xu , Jiabei Liu , Haopeng Zhao , Tianyao Zheng , Tongzhou Jiang , Lipeng Liu This is my paper · ORCID
classification cs.ROcs.LG
keywords deepalgorithmddpgautonomouslearningnavigationq-networkreinforcement
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper explores the method of achieving autonomous navigation of unmanned vehicles through Deep Reinforcement Learning (DRL). The focus is on using the Deep Deterministic Policy Gradient (DDPG) algorithm to address issues in high-dimensional continuous action spaces. The paper details the model of a Ackermann robot and the structure and application of the DDPG algorithm. Experiments were conducted in a simulation environment to verify the feasibility of the improved algorithm. The results demonstrate that the DDPG algorithm outperforms traditional Deep Q-Network (DQN) and Double Deep Q-Network (DDQN) algorithms in path planning tasks.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning

    cs.CL 2024-11 reject novelty 4.0 of 10

    An ensemble of two LoRA-finetuned 8-9B models gets 80.2% accuracy on Chatbot Arena preference prediction, slightly above GPT-4's 78.3%, but with no error bars or code.

  2. Artistic Neural Style Transfer Algorithms with Activation Smoothing

    cs.CV 2024-11 reject novelty 3.0 of 10

    Applying tanh, softsign, or scaling smoothing to ResNet activations yields stylization quality comparable to softmax-based SWAG, though the evidence is only qualitative.

  3. Real-time Video Target Tracking Algorithm Utilizing Convolutional Neural Networks (CNN)

    cs.CV 2024-11 reject novelty 2.0 of 10

    A vague proposal for a CNN plus optical flow tracking method with unverifiable performance claims.

  4. Enhanced Recommendation Combining Collaborative Filtering and Large Language Models

    cs.AI 2024-12 reject novelty 1.0 of 10

    A simple weighted sum of collaborative filtering scores and LLM text embeddings is claimed to improve recommendation accuracy, but the reported experiments are not reproducible.

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