Enroll Course: https://www.udemy.com/course/apprentissage-par-renforcement-avec-python-partie-2/
In the ever-evolving landscape of artificial intelligence, Reinforcement Learning (RL) stands out as a powerful paradigm for unlocking machine creativity. Unlike humans, AI can leverage massive parallel processing to perform thousands of attempts simultaneously, a capability amplified by sophisticated algorithms. Following up on the foundational “Apprentissage par renforcement avec Python – Partie 1,” this second installment, “Apprentissage par renforcement avec Python – Partie 2,” is designed to elevate your RL expertise.
This comprehensive course expertly guides you from the finite environments covered previously to the complexities of infinite, episodic, and continuous tasks. You’ll gain hands-on experience coding function approximations, both linear and non-linear, using cutting-edge libraries like Keras and TensorFlow. The curriculum delves into the practical implementation of on-policy algorithms such as Deep Sarsa and off-policy algorithms like Deep Q-Learning, among others.
The instructor has meticulously crafted this course with clear explanations and abundant examples, ensuring a deep understanding of algorithm construction and Python implementation. Spanning over 9 hours, the course is structured logically:
* **On-Policy Prediction with Approximations:** Start with basic linear function approximations for evaluating states in continuous environments, utilizing stochastic gradient descent and semi-gradient methods.
* **Building Linear Function Approximations:** Explore more advanced linear approximations, including multi-dimensional polynomial and Fourier bases.
* **Non-Linear Function Approximation with Neural Networks:** Harness the power of neural networks with Keras and TensorFlow to approximate non-linear functions, integrating them into previously learned algorithms.
* **On-Policy Control with Approximations:** Transition to control problems, learning optimal strategies using linear approximations of state values, and revisiting continuous versions of Sarsa and Sarsa n-step.
* **Deep Sarsa (On-Policy Control):** Master Deep Sarsa for optimizing strategies with neural network-approximated functions in online learning scenarios.
* **Off-Policy Methods with Approximations:** Tackle the challenges of off-policy learning, where data is collected from a different policy, and explore theoretical steps for stable and convergent algorithms.
* **Deep Q-Learning (Off-Policy Control):** Conclude by applying neural networks to optimize strategies in off-policy learning using the powerful Deep Q-Learning algorithm.
While prior Python knowledge is beneficial, and a grasp of probability and vector spaces will enhance theoretical understanding, the course is accessible. Experiments are conducted using Jupyter notebooks, and resources like Google Colab are recommended for a seamless experience. All necessary resources are provided.
If you’re looking to advance your skills in reinforcement learning and tackle more complex, real-world problems, this course is an exceptional recommendation. It bridges the gap between foundational concepts and advanced techniques, equipping you with the practical skills to implement sophisticated RL algorithms in Python.
Enroll Course: https://www.udemy.com/course/apprentissage-par-renforcement-avec-python-partie-2/