Enroll Course: https://www.udemy.com/course/ittensive-python-reinforcement-learning/
Reinforcement Learning (RL) is a fascinating area of machine learning where agents learn to make decisions by interacting with an environment and receiving rewards or penalties. If you’re looking to dive deep into this field, the “Машинное обучение с подкреплением на Python” (Reinforcement Learning with Python) course on Udemy, offered by ITtensive, is an excellent choice. Please note that to access ITtensive courses on Udemy, you’ll need to contact support@ittensive.com with your desired course name.
This course is the capstone of ITtensive’s Machine Learning program and offers a robust, hands-on approach to understanding and implementing RL algorithms. It covers three key projects that provide practical experience:
1. **Tic-Tac-Toe on a 3×3 Board:** This module breaks down the classic game into an RL problem. You’ll learn to program the game environment, define winning conditions, and train agents (players) to achieve a draw. The course delves into fundamental strategies like the Bellman equation, Q-learning, and policy iteration. You’ll compare the effectiveness of various strategies, including epsilon-greedy and optimized epsilon-greedy, by having them play against each other. The project involves developing your own winning agent for Tic-Tac-Toe.
2. **CartPole Balancing:** Utilizing the AI Gym framework, this section focuses on the CartPole problem, where the goal is to balance a pole on a moving cart. You’ll learn to train an agent using sensor data. A significant part of this module is dedicated to understanding and implementing Deep Q-Networks (DQN), a powerful neural network architecture for RL, to accelerate and stabilize learning. The course explores different learning approaches, including random processes, learning state distributions (prior and posterior probabilities), and simulating short-term and long-term memory. You’ll also tackle challenges in training and optimizing fully connected neural networks. The project here is to develop an optimized DQN for balancing the cart.
3. **Blackjack (21):** This module uses the AI Gym environment to calculate optimal moves in Blackjack. You’ll explore Monte Carlo methods, including single and multiple importance sampling, on-policy and off-policy learning, and optimizing exploration starts. The course even visualizes the optimal agent behavior through state-space isopurfaces. Your project will be to compute the optimal Blackjack playing strategy.
**Theoretical Foundations:**
The course also provides a solid theoretical grounding in RL, covering essential concepts such as:
* Introduction to Machine Learning and Reinforcement Learning Tasks
* Reinforcement Learning Metrics
* The Exploration-Exploitation Trade-off
* Markov Decision Processes (MDPs)
* The Bellman Equation and Principle
* Monte Carlo Methods
* Q-Tables and Q-Learning
* Epsilon-Greedy Strategies (including decaying epsilon)
* Upper Confidence Bound (UCB) Strategy
* Thompson Sampling
* Deep Q-Networks (DQN) Creation and Training
* Short-term and Long-term Memory
* On-policy and Off-policy Learning
**Recommendation:**
“Машинное обучение с подкреплением на Python” is a highly recommended course for anyone serious about learning Reinforcement Learning. The combination of theoretical depth and practical, project-based learning makes it incredibly effective. The progression through increasingly complex problems, from a simple game to a classic control task and then to a card game, is well-structured. The emphasis on understanding the ‘why’ behind the algorithms, coupled with the ‘how’ of implementation, makes this course a standout. If you’re looking to build a strong foundation in RL and gain practical skills, this ITtensive course is an excellent investment.
Enroll Course: https://www.udemy.com/course/ittensive-python-reinforcement-learning/