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制作一份中文演示文稿，主题为《强化学习在无人船上的应用》。风格为简洁海洋蓝。适用于课堂汇报，无需演讲提示。共20页，内容结构如下： 1. 封面页：标题、作者、学校、日期 2. 无人船简介与应用场景 3. 无人船自主控制的意义 4. 强化学习引入的必要性 5. 强化学习基本原理（Agent、环境、奖励） 6. Q-Learning与DQN算法简介 7. 连续控制中的PPO算法 8. RL与传统控制方法对比 9. 无人船动力学模型 10. 控制目标定义（路径规划、避障、节能） 11. 状态与动作空间设计 12. 奖励函数设计 13. 仿真环境搭建（Gazebo/MATLAB/ROS） 14. 训练过程与结果 15. 收敛曲线与性能分析 16. 与传统方法对比实验 17. 实际案例：港口自动巡航 18. 当前挑战（样本效率、安全性） 19. 未来展望（多智能体、迁移学习） 20. 总结与致谢
Explores reinforcement learning (RL) for USVs: basics (Q-Learning, DQN, PPO), dynamics modeling, state/action/reward design, Gazebo sim training (95% success), vs. traditional methods, port case, chal
This title slide is titled "强化学习在无人船上的应用" (Application of Reinforcement Learning on Unmanned Ships). The subtitle lists the author XXX from XXX University, dated October 2023, for a class presentation.
作者：XXX | 学校：XXX大学 | 日期：2023年10月 | 课堂汇报
Source: 作者：XXX | 学校：XXX大学 | 日期：2023年10月 | 课堂汇报
Unmanned Surface Vehicles (USVs) are ships that navigate autonomously without human intervention. The slide outlines their applications in ocean monitoring, search and rescue, military patrol, and port logistics, plus core advantages of efficiency, safety, and 24-hour continuous operation.
The slide "无人船自主控制的意义" outlines the key benefits of autonomous control for unmanned ships, including improved navigation safety by avoiding human errors and adaptation to complex environments like bad weather. It also highlights cost reduction, higher task efficiency, and advancement of marine intelligent technologies.
Traditional methods struggle to handle dynamic, uncertain environments, necessitating reinforcement learning (RL), which adaptively learns optimal strategies through trial and error. RL excels in high-dimensional continuous state spaces while boosting robustness and generalization.
This slide introduces reinforcement learning basics, defining the agent (e.g., unmanned ship controller) and environment (ocean scenarios with dynamics models). It covers core elements—state (s), action (a), reward (r)—and the goal of maximizing cumulative rewards (∑ rt).
The slide introduces Q-Learning, a tabular method for discrete action spaces that updates a Q-table via the formula Q(s,a) ← Q(s,a) + α[r + γ max{a'} Q(s',a') - Q(s,a)], but struggles with high-dimensional states. It contrasts this with DQN, which uses deep neural networks to approximate the Q-function for complex environments, enhanced by experience replay and target networks for better stability and performance.
| Q-Learning（表格型，离散动作） | DQN（深度Q网络，神经网逼近） |
|---|
| 适用于离散动作空间，使用Q表存储状态-动作值函数。 更新公式： Q(s,a) ← Q(s,a) + α[r + γ max{a'} Q(s',a') - Q(s,a)] 简单直观，但高维状态空间下存储困难。 | 使用深度神经网络逼近Q函数，处理连续或高维状态。 关键改进：经验回放（稳定训练）、目标网络（减少相关性）。 显著提升复杂环境下的稳定性和性能。 |
The slide introduces PPO (Proximal Policy Optimization) as a policy gradient method under the Actor-Critic framework. It highlights PPO's clipped objective function for stable training and its suitability for continuous thrust/rudder control in unmanned ships.
Source: 强化学习在无人船上的应用
This slide compares RL with traditional control methods PID and MPC via a table listing advantages and disadvantages. PID is simple and fast but needs a precise model; MPC optimizes globally but is computationally intensive; RL is adaptive and model-free but training is time-consuming.
{ "headers": [ "方法", "优点", "缺点" ], "rows": [ [ "PID", "简单快速", "需精确模型" ], [ "MPC", "优化全局", "计算密集" ], [ "RL", "自适应无模型", "训练耗时" ] ] }
The slide, titled "Unmanned Ship Dynamics Model," outlines the MMG model for hull, propeller, and rudder systems. It includes equations for longitudinal force (X = m(ú - vr)), stern rudder moment (N = Iż), and the Nomoto simplified model for heading dynamics.
Source: Image from Wikipedia article "Unmanned surface vehicle"
The slide "控制目标定义" defines three key control objectives. They include path planning to track preset routes, real-time obstacle avoidance, and energy saving via speed optimization to minimize consumption.
The slide defines the state space as position (x,y), heading ψ, speeds u,v, and obstacle relative distances, with a continuous action space of thrust F and rudder angle δ. It emphasizes normalization for unified scales to boost training efficiency, ensuring comprehensive state observations and controllable actions.
The slide presents a four-stage workflow for designing a reward function in unmanned boat control. It covers defining components (trajectory error, collision penalties, speed rewards, energy costs), formulating a weighted linear equation with thresholds, tuning weights experimentally, and validating via simulations for convergence, obstacle avoidance, and energy optimization.
{ "headers": [ "阶段", "任务描述", "关键细节" ], "rows": [ [ "1. 定义奖励组件", "识别无人船控制的关键因素", "轨迹误差、碰撞惩罚、速度奖励、能耗惩罚" ], [ "2. 设计奖励公式", "构建加权线性组合及分段机制", "r = w1(-轨迹误差) + w2(-碰撞) + w3(速度) + w4(-能耗)；航行+100，碰撞-1000" ], [ "3. 参数调优", "调整权重系数w1~w4", "通过初步实验平衡各组件影响" ], [ "4. 验证迭代", "仿真测试奖励函数效果", "确保训练收敛、避障优先、节能优化" ] ] }
Source: r = w1 (-轨迹误差) + w2 (-碰撞惩罚) + w3 (速度奖励) + w4 (-能耗)。分段设计：航行+100，碰撞-1000。
This slide on simulation environment setup features Gazebo + ROS for physical and sensor simulation, plus MATLAB/Simulink for rapid prototyping. It also integrates Stable-Baselines3 for reinforcement learning algorithms and Gym for standardized environment interfaces.
The slide, titled "Training Process and Results," depicts the cycle of environment interaction → experience collection → strategy update. It highlights key outcomes: success rate >95% and path error <1m.
Source: Image from Wikipedia article "Reinforcement learning"
The slide highlights a 98% obstacle avoidance success rate and a path tracking MSE of just 0.5. It also shows fast convergence of cumulative rewards to a stable value alongside a significant, smooth decline in the loss function during training.
障碍规避成功率高达98%
路径跟踪均方误差仅0.5
快速收敛至稳定值
训练中损失平稳下降
This slide compares PID, APF, and RL methods for path planning against traditional approaches, evaluating path error (m), obstacle avoidance rate, and time (s). RL excels with the lowest error (0.8m) and highest avoidance rate (98%), despite longer computation time (150s) compared to PID (2.1m, 85%, 120s) and APF (1.5m, 90%, 110s).
{ "headers": [ "方法", "路径误差(m)", "避障率", "时间(s)" ], "rows": [ [ "PID", "2.1", "85%", "120" ], [ "APF", "1.5", "90%", "110" ], [ "RL", "0.8", "98%", "150" ] ] }
This slide showcases a real-world case of an unmanned boat autonomously cruising in a port scene. It highlights real-time avoidance of surrounding vessels and precise berthing operations.
Source: Image from Wikipedia article "Unmanned surface vehicle"
The slide "当前挑战" (Current Challenges) outlines major limitations in the system. These include low sample efficiency requiring vast training data, insufficient safety with high exploration risks, poor real-time performance from inference delays, and weak generalization across scenarios.
The slide "未来展望" (Future Outlook) outlines key future research directions. It covers multi-agent RL for formation collaboration, sim2real transfer learning, integration of large models for advanced perception, and standardized framework development.
The conclusion slide states that reinforcement learning significantly enhances unmanned ships' autonomous capabilities. It thanks the audience for listening, invites Q&A, and provides a contact email (xxx@email.com).
强化学习显著提升无人船自主能力。
感谢聆听！Q&A。联系：xxx@email.com
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