Dynamic Path Planning of Unknown Environment Based on Deep Reinforcement Learning
Pygame module in PYTHON is used to establish dynamic environments. 以激光雷达信号和局部目标位置为输入，利用卷积神经网络(CNNs)对环境状态进行泛化,而Q-learning增强了环境中动态避障和智能体局部规划的能力.结果表明，经过不同动态环境的训练和新环境的测试，该agent能够在未知的动态环境中成功地到达局部目标位置。 之前的工
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相关文章
- 1. A Novel GRU-RNN Network Model for Dynamic Path Planning of Mobile Robot
- 2. [Reinforcement Learning] 动态规划(Planning)
- 3. A Survey of Recommender Systems Based on Deep Learning （1）
- 4. Summary of defect detection algorithms based on deep learning
- 5. PR10.10:#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
- 6. Deep Reinforcement Learning
- 7. Reinforcement learning: integrating learning and planning, exploitation and exploration
- 8. Reinforcement Learning: Planning by DP
- 9. Planning by Dynamic Programming
- 10. Reinforcement Learning（二）：Value-Based