Training: Reinforcement Learning – Learning through experience
Level
IntermediateDuration
24h / 3 daysDate
Individually arrangedPrice
Individually arrangedTraining: Reinforcement Learning – Learning Through Experience
Reinforcement Learning (RL) is a cutting-edge area of artificial intelligence focused on machine learning through interaction with the environment and the accumulation of experience. This intensive, hands-on training introduces participants to the fundamentals and key algorithms of RL while providing the skills to implement working models independently. It is ideal for developers, data analysts, and AI enthusiasts who want to understand how machines learn to make decisions based on their own experiences. Enter the world of reinforcement learning and gain in-demand competencies that are becoming increasingly sought after on the job market.
Developers and data analysts looking to expand their skills with practical reinforcement learning applications-
Professionals working on AI development, decision-making algorithms, and process automation
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Data Science, Machine Learning, and automation specialists who want to explore next-generation AI tools
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Technology enthusiasts eager to discover modern machine learning methods
What will you learn?
- The fundamentals of reinforcement learning and its real-world applications
- How to design RL environments and implement reinforcement learning algorithms in Python
- How to analyze and optimize the learning process of agents in different scenarios
- How to build modern AI systems using experience-based learning — from simple examples to advanced projects
- Real-world RL use cases, opening opportunities for new projects in AI, automation, and data analysis
Program
Day 1: Introduction and Foundations of Reinforcement Learning
- Module 1: Introduction to RL
- What is RL and how does it differ from other machine learning techniques
- Key concepts: agent, environment, actions, rewards, policy, value function
- Comparison with supervised and unsupervised learning tasks
- Intuitive examples (board games, robot control, recommendation systems) to illustrate RL in practice
- Module 2: Mathematical Models of RL
- Markov Decision Processes (MDP)
- Bellman equations and their importance
- Overview of basic algorithms: Dynamic Programming
- Practical exercises with RL simulators (OpenAI Gym, TensorFlow Agents)
- Extended workshop: creating a custom environment (e.g., production line control, movie recommendation system, website traffic optimization) and defining agent reward rules
Day 2: Classical Algorithms and Practical Applications
- Module 3: Value-Based Learning
- Q-Learning, SARSA, Monte Carlo methods — theory and implementation
- Exploration vs. exploitation strategies (epsilon-greedy, softmax, UCB)
- Workshop: building an RL agent to optimize warehouse flow, simulating logistics scenarios and analyzing exploration strategies
- Module 4: Policy-Based Learning and Actor-Critic Methods
- Direct approaches to policy optimization
- Introduction to actor-critic methods and implementation
- Practical exercise: RL-based ad budget allocation — training an agent to optimize campaign spending
Day 3: Advanced Methods and Practical Workshops
- Module 5: Modern RL Techniques
- Deep Reinforcement Learning — combining RL with neural networks
- Overview of frameworks and libraries (OpenAI Gym, Stable Baselines)
- Challenges of scaling RL algorithms to high-dimensional problems
- Real-world use cases: Atari gameplay, autonomous driving, financial process optimization, user behavior analysis
- Module 6: Hands-On Workshop
- Implementing a simple RL agent from scratch in Python — building, training, and testing
- Analyzing results and tuning hyperparameters (learning rate, discount factor, epsilon decay)
- Comparing algorithms (Q-Learning vs. Deep Q-Network) in the same environment to evaluate effectiveness
- Discussion: challenges and best practices in RL projects
Contact us
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