Technologies / Artificial Intelligence

The basics of teaching with reinforcement

The training introduces participants to the fascinating world of Reinforcement Learning, one of the most rapidly growing areas of artificial intelligence. The workshop program leads from fundamental concepts to advanced techniques for implementing agents that learn by interacting with the environment. Through a combination of theory and intensive hands-on exercises, participants learn to design, implement and optimize reinforcement learning systems in real-world applications.

Issues

Theory of learning with reinforcement
Markov decision processes
Q-learning algorithms
Policy Gradient Methods
Deep Reinforcement Learning
Multi-agent systems
Optimization of hyperparameters
Simulation environments
Transfer Learning
Exploration and exploitation
Debugging agents
Production deployments

Benefits

The participant will gain a thorough theoretical and practical knowledge of reinforcement learning and its applications
Will develop the ability to design and implement learning systems by interacting with the environment
Will master techniques for implementing and tuning algorithms for learning with reinforcement
Will learn to solve complex decision-making problems using RL methods
Will learn how to optimize and debug learning agents
Will be able to implement reinforcement learning systems in real-world applications

Who is this training for?

Data scientists interested in learning with reinforcement

ML engineers developing decision-making systems

AI developers implementing learning agents

Researchers and scientists in the field of AI

Process optimization specialists

Developers of autonomous systems

Prerequisites

Sound knowledge of mathematics and statistics
Experience in Python programming
Basic knowledge of machine learning
Knowledge of the basics of deep learning

Training program

Markov decision processes
Models of agents and environments
Exploration and exploitation strategies

Q-learning and SARSA
Policy Gradient Methods
Temporal Difference Learning

Deep Reinforcement Learning
Actor-Critic Architectures

Transfer Learning
Implementation and optimization
Simulation environments
Techniques for debugging agents
Optimization of hyperparameters
Implementation in production

Delivery Methods

Online

Convenience of participating from anywhere
Interactive live sessions with trainer
Materials available for 30 days
No travel costs

On-site

Direct contact with trainer and group
Intensive hands-on workshops
Networking with other participants
Full focus on learning

Frequently asked questions

What are the prerequisites for this training?

For The basics of teaching with reinforcement we recommend: Sound knowledge of mathematics and statistics; Experience in Python programming; Basic knowledge of machine learning.

What is the format and duration of this training?

The training lasts 3 days and is available in online and on-site format. Sessions run from 9:00 AM to 4:00 PM. We can also customize the schedule to fit your team's needs.

Who is this training designed for?

This training is designed for: Data scientists interested in learning with reinforcement; ML engineers developing decision-making systems; AI developers implementing learning agents.