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.

Duration

24h · 3 days

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Who it's for

Ideal for teams that…

1 Developers and data analysts looking to expand their skills with practical reinforcement learning applications

2 Professionals working on AI development, decision-making algorithms, and process automation

3 Data Science, Machine Learning, and automation specialists who want to explore next-generation AI tools

4 Technology enthusiasts eager to discover modern machine learning methods

Outcomes after the program

Hands-on AI and data analytics workshops — built around your team's real cases.

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The fundamentals of reinforcement learning and its real-world applications

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How to design RL environments and implement reinforcement learning algorithms in Python

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How to analyze and optimize the learning process of agents in different scenarios

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How to build modern AI systems using experience-based learning — from simple examples to advanced projects

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Real-world RL use cases, opening opportunities for new projects in AI, automation, and data analysis

Program · 6 modules

What we actually do

Day 1: Introduction and Foundations of Reinforcement Learning

M01

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

M02

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

M03

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

M04

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

M05

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

M06

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

Every module is adapted to your stack and context. The above is a starting point — not a fixed agenda.

How we work

From brief to retro in 30 days.

Brief & diagnosis

A call with the team lead + a short survey for participants. We define goals, gap and context.

Program customization

We adapt modules, case studies and code examples to your stack. Approval in 5 days.

Workshop

Trainer-led sessions, hands-on, code review. Mentor available between sessions too.

Retro + report

Outcome report for the team and lead. 30 days of consulting included.

Inquiry

Send a brief. We'll reply within 1 day.

After a short brief we'll prepare a program and a quote. No obligations — it's just a starting point.

✓Quote within 48h of the brief

✓First session within 30 days

✓Pilot before the full decision

✓VAT invoice, payment in instalments possible

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