Enroll Course: https://www.udemy.com/course/ittensive-python-reinforcement-learning/
If you’re looking to dive deep into the world of reinforcement learning, the course “Машинное обучение с подкреплением на Python” by ITtensive on Udemy is a fantastic choice. This course is designed for those who have a foundational understanding of machine learning and want to expand their knowledge into the exciting field of reinforcement learning.
### Course Overview
The course covers three main tasks that are pivotal in understanding reinforcement learning:
1. **Tic-Tac-Toe Game**: You will program an environment for a 3×3 Tic-Tac-Toe game, learn the winning conditions, and train both simple and complex agents to achieve a draw. This part introduces you to fundamental strategies such as the Bellman equation, Q-learning, and pursuit learning, while also comparing the effectiveness of various strategies like epsilon-greedy and optimized epsilon-greedy approaches. The project focuses on developing your own winning agent for the game.
2. **Balancing a Cart in a Physical Environment**: Using the CartPole AI Gym, you’ll learn how to balance a cart based on sensor data. This section introduces the principles of building a reinforcement learning neural network (DQN = Deep Q-Network) and how to use it to speed up and stabilize the learning process. You will explore agent training through random processes and state distribution studies, as well as the challenges of training and optimizing a fully connected neural network. The project here involves developing an optimized DQN for cart balancing.
3. **Blackjack (21 Points)**: In this module, you’ll make optimal moves in a game of Blackjack using the AI Gym environment. You will utilize Monte Carlo methods, including single and multiple hits, unified and split policies, and optimize exploratory starts. The project culminates in calculating the optimal strategy for playing Blackjack, with visualizations of the agent’s optimal behavior policy through isosurfaces in the state space.
### Theoretical Foundations
The course is not just about coding; it also delves into essential theoretical concepts such as:
– Reinforcement learning tasks and metrics
– Exploration vs. exploitation dilemma
– Markov decision processes
– Bellman principles and equations
– Monte Carlo methods and Q-tables
– Epsilon-greedy strategies and more.
### Why You Should Enroll
This course is ideal for those who want to grasp the core concepts of reinforcement learning while applying them in practical scenarios. The hands-on projects help solidify your understanding, and the theoretical knowledge provided is crucial for anyone looking to excel in machine learning. Moreover, the support from ITtensive ensures that you have guidance throughout your learning journey.
In conclusion, if you’re eager to enhance your skills in Python and machine learning, particularly in reinforcement learning, the “Машинное обучение с подкреплением на Python” course is highly recommended. It offers a balanced mix of theory and practical application that will prepare you for real-world challenges in AI.
### Tags
– Reinforcement Learning
– Machine Learning
– Python
– AI Gym
– Deep Learning
– Q-Learning
– Monte Carlo Methods
– Data Science
– ITtensive
– Online Courses
### Topic
Reinforcement Learning in Python
Enroll Course: https://www.udemy.com/course/ittensive-python-reinforcement-learning/