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 potent method for unlocking machine creativity. Unlike humans, AI can execute countless simulations concurrently, a feat made possible by running parallel algorithms on robust computational infrastructure. Building upon the foundational concepts introduced in its predecessor, ‘Apprentissage par renforcement avec Python – Partie 1’, this second installment delves deeper into the practical application of RL.
This comprehensive course, spanning over 9 hours, expertly guides you through extending previously learned methods from finite environments to infinite ones, tackling both episodic and continuous tasks. You’ll gain hands-on experience coding function approximations, both linear and non-linear, using powerful tools like Keras and TensorFlow. The curriculum meticulously covers on-policy algorithms such as Deep Sarsa and off-policy algorithms like Deep Q-Learning, among others.
The instructor has a clear commitment to clarity, providing thorough explanations and numerous examples that demystify the construction and implementation of these complex algorithms in Python. The course is structured logically, covering:
* **On-policy Prediction with Approximations:** Understanding linear function approximation for evaluating state values in continuous environments, utilizing stochastic gradient descent and semi-gradient methods.
* **Building Linear Function Approximators:** Exploring more sophisticated linear approximations using polynomial and multi-dimensional Fourier bases.
* **Non-linear Function Approximation with Neural Networks:** Leveraging Keras and TensorFlow to build and integrate neural networks into RL algorithms.
* **On-policy Control with Approximations:** Implementing optimal strategies based on linear state-value approximations, including continuous versions of Sarsa and Sarsa n-step.
* **Deep Sarsa (On-policy Control):** Mastering Deep Sarsa for optimizing strategies using neural network approximations in online learning scenarios.
* **Off-policy Methods with Approximations:** Addressing the stability and convergence challenges of off-policy learning with linear function approximations and exploring new methods tailored for this context.
* **Deep Q-Learning (Off-policy Control):** Applying neural networks to optimize strategies in off-policy learning with the powerful Deep Q-Learning algorithm.
While prior Python knowledge is beneficial, the course also provides theoretical explanations requiring some background in mathematics, particularly probability, and vector spaces. All experiments are conducted using Jupyter notebooks, and resources for environments like Google Colab are readily available. This course is an invaluable resource for anyone looking to gain practical, in-depth knowledge of advanced Reinforcement Learning techniques.
Enroll Course: https://www.udemy.com/course/apprentissage-par-renforcement-avec-python-partie-2/