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課程簡介
Introduction to Robot Learning
- Overview of machine learning in robotics
- Supervised vs unsupervised vs reinforcement learning
- Applications of RL in control, navigation, and manipulation
Fundamentals of Reinforcement Learning
- Markov decision processes (MDP)
- Policy, value, and reward functions
- Exploration vs exploitation trade-offs
Classical RL Algorithms
- Q-learning and SARSA
- Monte Carlo and temporal difference methods
- Value iteration and policy iteration
Deep Reinforcement Learning Techniques
- Combining deep learning with RL (Deep Q-Networks)
- Policy gradient methods
- Advanced algorithms: A3C, DDPG, and PPO
Simulation Environments for Robot Learning
- Using OpenAI Gym and ROS 2 for simulation
- Building custom environments for robotic tasks
- Evaluating performance and training stability
Applying RL to Robotics
- Learning control and motion policies
- Reinforcement learning for robotic manipulation
- Multi-agent reinforcement learning in swarm robotics
Optimization, Deployment, and Real-World Integration
- Hyperparameter tuning and reward shaping
- Transferring learned policies from simulation to reality (Sim2Real)
- Deploying trained models on robotic hardware
Summary and Next Steps
最低要求
- An understanding of machine learning concepts
- Experience with Python programming
- Familiarity with robotics and control systems
Audience
- Machine learning engineers
- Robotics researchers
- Developers building intelligent robotic systems
21 時間：
客戶評論 (1)
它對人工智慧的知識和利用在未來Robotics。
Ryle - PHILIPPINE MILITARY ACADEMY
課程 - Artificial Intelligence (AI) for Robotics
機器翻譯
