深度强化学习在智能机器人决策中的优化
摘 要
随着人工智能技术的发展，智能机器人在复杂环境下的自主决策能力成为研究热点。深度强化学习融合了深度学习的感知能力和强化学习的决策能力，为智能机器人的决策优化提供了新思路。本文旨在探讨深度强化学习在智能机器人决策中的应用，通过构建基于深度Q网络（DQN）改进模型，引入双Q网络与优先经验回放机制，解决传统算法中价值估计偏差大、训练效率低的问题。实验选取具有代表性的迷宫导航任务和机械臂抓取任务作为测试场景，利用所提方法对机器人进行训练。结果表明，在相同训练次数下，改进后的算法使机器人能够更快地收敛到最优策略，决策成功率分别提高了25%和18%，且稳定性显著增强。
关键词：深度强化学习 智能机器人决策 双Q网络
Abstract
With the development of artificial intelligence technology, the autonomous decision-making ability of intelligent robots in the complex environment has become a research hotspot. Deep reinforcement learning integrates the perception ability of deep learning and the decision-making ability of reinforcement learning, providing a new idea for the decision optimization of intelligent robots. This paper aims to explore the application of deep reinforcement learning in the decision-making of intelligent robots. By constructing the improvement model based on deep Q network (DQN), dual Q network and priority experience playback mechanism are introduced, to solve the problems of large value estimation deviation and low training efficiency in traditional algorithms. Representative maze navigation task and robotic arm grasping task were selected as test scenarios, and the robot was trained with the proposed method. The results show that the improved algorithm enables the robot to converge to the optimal strategy faster with the same training times, and the decision success rate by 25% and 18%, respectively, and the stability is significantly enhanced.
Keyword:Deep Reinforcement Learning Intelligent Robot Decision-Making Double Q-Network
目 录
1绪论 1
1.1深度强化学习与智能机器人决策的背景 1
1.2国内外研究现状综述 1
1.3本文研究方法概述 2
2深度强化学习算法在机器人决策中的应用 2
2.1算法选择与适应性分析 2
2.2关键技术实现路径 3
2.3应用案例剖析 3
3决策优化模型构建与评估 4
3.1模型架构设计原则 4
3.2性能评价指标体系 4
3.3实验验证与结果分析 5
4智能机器人决策系统的实际部署 6
4.1部署环境要求 6
4.2系统集成方案 7
4.3运行效果反馈与改进 7
结论 8
参考文献 10
致谢 11
摘 要
随着人工智能技术的发展，智能机器人在复杂环境下的自主决策能力成为研究热点。深度强化学习融合了深度学习的感知能力和强化学习的决策能力，为智能机器人的决策优化提供了新思路。本文旨在探讨深度强化学习在智能机器人决策中的应用，通过构建基于深度Q网络（DQN）改进模型，引入双Q网络与优先经验回放机制，解决传统算法中价值估计偏差大、训练效率低的问题。实验选取具有代表性的迷宫导航任务和机械臂抓取任务作为测试场景，利用所提方法对机器人进行训练。结果表明，在相同训练次数下，改进后的算法使机器人能够更快地收敛到最优策略，决策成功率分别提高了25%和18%，且稳定性显著增强。
关键词：深度强化学习 智能机器人决策 双Q网络
Abstract
With the development of artificial intelligence technology, the autonomous decision-making ability of intelligent robots in the complex environment has become a research hotspot. Deep reinforcement learning integrates the perception ability of deep learning and the decision-making ability of reinforcement learning, providing a new idea for the decision optimization of intelligent robots. This paper aims to explore the application of deep reinforcement learning in the decision-making of intelligent robots. By constructing the improvement model based on deep Q network (DQN), dual Q network and priority experience playback mechanism are introduced, to solve the problems of large value estimation deviation and low training efficiency in traditional algorithms. Representative maze navigation task and robotic arm grasping task were selected as test scenarios, and the robot was trained with the proposed method. The results show that the improved algorithm enables the robot to converge to the optimal strategy faster with the same training times, and the decision success rate by 25% and 18%, respectively, and the stability is significantly enhanced.
Keyword:Deep Reinforcement Learning Intelligent Robot Decision-Making Double Q-Network
目 录
1绪论 1
1.1深度强化学习与智能机器人决策的背景 1
1.2国内外研究现状综述 1
1.3本文研究方法概述 2
2深度强化学习算法在机器人决策中的应用 2
2.1算法选择与适应性分析 2
2.2关键技术实现路径 3
2.3应用案例剖析 3
3决策优化模型构建与评估 4
3.1模型架构设计原则 4
3.2性能评价指标体系 4
3.3实验验证与结果分析 5
4智能机器人决策系统的实际部署 6
4.1部署环境要求 6
4.2系统集成方案 7
4.3运行效果反馈与改进 7
结论 8
参考文献 10
致谢 11
