https://github.com/cvir-lab/ppf_framework
Science Score: 26.0%
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Low similarity (7.7%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: CVIR-Lab
- License: apache-2.0
- Language: Python
- Default Branch: main
- Size: 1.49 MB
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Metadata Files
README.md
Dynamic-Target Potential Pursuit Field Reward for UAV Reinforcement Learning
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| The proposed PPF-based hierarchical reinforcement learning framework |
This is the official for manuscript entitled Dynamic-Target Pursuit Potential Field Reward for UAV Reinforcement Learning submitted to IEEE Transactions on Control Systems Technology.
Potential Pursuit Field (PPF), a novel reward shaping framework aimed to address the reward sparsity in reinforcement learning for dynamic target pursuit. By designing a droplet-shaped anisotropic potential field, the proposed PPF model provides dense and direction-aware reward signals while preserving policy invariance through potential-based reward shaping. Building upon PPF, we developed a hierarchical reinforcement learning algorithm, enabling target pursuit and obstacle avoidance in non-line-of-sight(NLOS) environments, simultaneously.
Video Demo
Potential Pursuit Field(PPF)
Art work of Potential Pursuit Field (PPF)
A novel concept of the Potential Pursuit Field (PPF) is proposed to support a continuous and dense reward-shaping function, which can capture anisotropic features and obtain richer gradient information than that of traditional rewards.
Obstacle-free pursuit
Pursuit with Obstacle envirionment
The entire code and corresponding simulation environment will be released later.
Owner
- Name: CVIR-Lab
- Login: CVIR-Lab
- Kind: organization
- Repositories: 2
- Profile: https://github.com/CVIR-Lab
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