Training: Reinforcement Learning – Learning through experience

Level

Intermediate

Duration

24h / 3 days

Date

Individually arranged

Price

Individually arranged

Training: 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.

Who is this training for?

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

u003cbu003eDay 1: Introduction and Foundations of Reinforcement Learningu003c/bu003ernu003culu003ern tu003cli style=u0022font-weight: 400;u0022 aria-level=u00221u0022u003eu003cbu003eModule 1: Introduction to RLu003c/bu003eu003cbu003ernrnu003c/bu003ernu003culu003ern tu003cli style=u0022font-weight: 400;u0022 aria-level=u00222u0022u003eu003cspan style=u0022font-weight: 400;u0022u003eWhat is RL and how does it differ from other machine learning techniquesu003c/spanu003eu003cspan style=u0022font-weight: 400;u0022u003ernrnu003c/spanu003eu003c/liu003ern tu003cli style=u0022font-weight: 400;u0022 aria-level=u00222u0022u003eu003cspan style=u0022font-weight: 400;u0022u003eKey concepts: agent, environment, actions, rewards, policy, value functionu003c/spanu003eu003cspan style=u0022font-weight: 400;u0022u003ernrnu003c/spanu003eu003c/liu003ern tu003cli style=u0022font-weight: 400;u0022 aria-level=u00222u0022u003eu003cspan style=u0022font-weight: 400;u0022u003eComparison with supervised and unsupervised learning tasksu003c/spanu003eu003cspan style=u0022font-weight: 400;u0022u003ernrnu003c/spanu003eu003c/liu003ern tu003cli style=u0022font-weight: 400;u0022 aria-level=u00222u0022u003eu003cspan style=u0022font-weight: 400;u0022u003eIntuitive examples (board games, robot control, recommendation systems) to illustrate RL in practiceu003c/spanu003eu003cspan style=u0022font-weight: 400;u0022u003ernrnu003c/spanu003eu003c/liu003ernu003c/ulu003ernu003c/liu003ern tu003cli style=u0022font-weight: 400;u0022 aria-level=u00221u0022u003eu003cbu003eModule 2: Mathematical Models of RLu003c/bu003eu003cbu003ernrnu003c/bu003ernu003culu003ern tu003cli style=u0022font-weight: 400;u0022 aria-level=u00222u0022u003eu003cspan style=u0022font-weight: 400;u0022u003eMarkov Decision Processes (MDP)u003c/spanu003eu003cspan style=u0022font-weight: 400;u0022u003ernrnu003c/spanu003eu003c/liu003ern tu003cli style=u0022font-weight: 400;u0022 aria-level=u00222u0022u003eu003cspan style=u0022font-weight: 400;u0022u003eBellman equations and their importanceu003c/spanu003eu003cspan style=u0022font-weight: 400;u0022u003ernrnu003c/spanu003eu003c/liu003ern tu003cli style=u0022font-weight: 400;u0022 aria-level=u00222u0022u003eu003cspan style=u0022font-weight: 400;u0022u003eOverview of basic algorithms: Dynamic Programmingu003c/spanu003eu003cspan style=u0022font-weight: 400;u0022u003ernrnu003c/spanu003eu003c/liu003ern tu003cli style=u0022font-weight: 400;u0022 aria-level=u00222u0022u003eu003cspan style=u0022font-weight: 400;u0022u003ePractical exercises with RL simulators (OpenAI Gym, TensorFlow Agents)u003c/spanu003eu003cspan style=u0022font-weight: 400;u0022u003ernrnu003c/spanu003eu003c/liu003ern tu003cli style=u0022font-weight: 400;u0022 aria-level=u00222u0022u003eu003cspan style=u0022font-weight: 400;u0022u003eExtended workshop: creating a custom environment (e.g., production line control, movie recommendation system, website traffic optimization) and defining agent reward rulesu003c/spanu003eu003cspan style=u0022font-weight: 400;u0022u003ernrnu003c/spanu003eu003c/liu003ernu003c/ulu003ernu003c/liu003ernu003c/ulu003ernu003cbu003eDay 2: Classical Algorithms and Practical Applicationsu003c/bu003ernu003culu003ern tu003cli style=u0022font-weight: 400;u0022 aria-level=u00221u0022u003eu003cbu003eModule 3: Value-Based Learningu003c/bu003eu003cbu003ernrnu003c/bu003ernu003culu003ern tu003cli style=u0022font-weight: 400;u0022 aria-level=u00222u0022u003eu003cspan style=u0022font-weight: 400;u0022u003eQ-Learning, SARSA, Monte Carlo methods — theory and implementationu003c/spanu003eu003cspan style=u0022font-weight: 400;u0022u003ernrnu003c/spanu003eu003c/liu003ern tu003cli style=u0022font-weight: 400;u0022 aria-level=u00222u0022u003eu003cspan style=u0022font-weight: 400;u0022u003eExploration vs. exploitation strategies (epsilon-greedy, softmax, UCB)u003c/spanu003eu003cspan style=u0022font-weight: 400;u0022u003ernrnu003c/spanu003eu003c/liu003ern tu003cli style=u0022font-weight: 400;u0022 aria-level=u00222u0022u003eu003cspan style=u0022font-weight: 400;u0022u003eWorkshop: building an RL agent to optimize warehouse flow, simulating logistics scenarios and analyzing exploration strategiesu003c/spanu003eu003cspan style=u0022font-weight: 400;u0022u003ernrnu003c/spanu003eu003c/liu003ernu003c/ulu003ernu003c/liu003ern tu003cli style=u0022font-weight: 400;u0022 aria-level=u00221u0022u003eu003cbu003eModule 4: Policy-Based Learning and Actor-Critic Methodsu003c/bu003eu003cbu003ernrnu003c/bu003ernu003culu003ern tu003cli style=u0022font-weight: 400;u0022 aria-level=u00222u0022u003eu003cspan style=u0022font-weight: 400;u0022u003eDirect approaches to policy optimizationu003c/spanu003eu003cspan style=u0022font-weight: 400;u0022u003ernrnu003c/spanu003eu003c/liu003ern tu003cli style=u0022font-weight: 400;u0022 aria-level=u00222u0022u003eu003cspan style=u0022font-weight: 400;u0022u003eIntroduction to actor-critic methods and implementationu003c/spanu003eu003cspan style=u0022font-weight: 400;u0022u003ernrnu003c/spanu003eu003c/liu003ern tu003cli style=u0022font-weight: 400;u0022 aria-level=u00222u0022u003eu003cspan style=u0022font-weight: 400;u0022u003ePractical exercise: RL-based ad budget allocation — training an agent to optimize campaign spendingu003c/spanu003eu003cspan style=u0022font-weight: 400;u0022u003ernrnu003c/spanu003eu003c/liu003ernu003c/ulu003ernu003c/liu003ernu003c/ulu003ernu003cbu003eDay 3: Advanced Methods and Practical Workshopsu003c/bu003ernu003culu003ern tu003cli style=u0022font-weight: 400;u0022 aria-level=u00221u0022u003eu003cbu003eModule 5: Modern RL Techniquesu003c/bu003eu003cbu003ernrnu003c/bu003ernu003culu003ern tu003cli style=u0022font-weight: 400;u0022 aria-level=u00222u0022u003eu003cspan style=u0022font-weight: 400;u0022u003eDeep Reinforcement Learning — combining RL with neural networksu003c/spanu003eu003cspan style=u0022font-weight: 400;u0022u003ernrnu003c/spanu003eu003c/liu003ern tu003cli style=u0022font-weight: 400;u0022 aria-level=u00222u0022u003eu003cspan style=u0022font-weight: 400;u0022u003eOverview of frameworks and libraries (OpenAI Gym, Stable Baselines)u003c/spanu003eu003cspan style=u0022font-weight: 400;u0022u003ernrnu003c/spanu003eu003c/liu003ern tu003cli style=u0022font-weight: 400;u0022 aria-level=u00222u0022u003eu003cspan style=u0022font-weight: 400;u0022u003eChallenges of scaling RL algorithms to high-dimensional problemsu003c/spanu003eu003cspan style=u0022font-weight: 400;u0022u003ernrnu003c/spanu003eu003c/liu003ern tu003cli style=u0022font-weight: 400;u0022 aria-level=u00222u0022u003eu003cspan style=u0022font-weight: 400;u0022u003eReal-world use cases: Atari gameplay, autonomous driving, financial process optimization, user behavior analysisu003c/spanu003eu003cspan style=u0022font-weight: 400;u0022u003ernrnu003c/spanu003eu003c/liu003ernu003c/ulu003ernu003c/liu003ern tu003cli style=u0022font-weight: 400;u0022 aria-level=u00221u0022u003eu003cbu003eModule 6: Hands-On Workshopu003c/bu003eu003cbu003ernrnu003c/bu003ernu003culu003ern tu003cli style=u0022font-weight: 400;u0022 aria-level=u00222u0022u003eu003cspan style=u0022font-weight: 400;u0022u003eImplementing a simple RL agent from scratch in Python — building, training, and testingu003c/spanu003eu003cspan style=u0022font-weight: 400;u0022u003ernrnu003c/spanu003eu003c/liu003ern tu003cli style=u0022font-weight: 400;u0022 aria-level=u00222u0022u003eu003cspan style=u0022font-weight: 400;u0022u003eAnalyzing results and tuning hyperparameters (learning rate, discount factor, epsilon decay)u003c/spanu003eu003cspan style=u0022font-weight: 400;u0022u003ernrnu003c/spanu003eu003c/liu003ern tu003cli style=u0022font-weight: 400;u0022 aria-level=u00222u0022u003eu003cspan style=u0022font-weight: 400;u0022u003eComparing algorithms (Q-Learning vs. Deep Q-Network) in the same environment to evaluate effectivenessu003c/spanu003eu003cspan style=u0022font-weight: 400;u0022u003ernrnu003c/spanu003eu003c/liu003ern tu003cli style=u0022font-weight: 400;u0022 aria-level=u00222u0022u003eu003cspan style=u0022font-weight: 400;u0022u003eDiscussion: challenges and best practices in RL projectsu003c/spanu003eu003c/liu003ernu003c/ulu003ernu003c/liu003ernu003c/ulu003e

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we will organize training for you tailored to your needs

Przemysław Wołosz

Key Account Manager

+48 730 830 801

przemyslaw.wolosz@infoShareAcademy.com

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