Technologies / Artificial Intelligence

Deep Reinforcement Learning with Python

The training introduces participants to the world of reinforcement learning using deep neural networks. The workshop program combines theoretical foundations of reinforcement learning with practical implementation in the Python environment. Participants, through systematic exercises, learn both classical algorithms and the latest developments in the field of deep reinforcement learning. The classes are conducted in the form of interactive workshops, where theory is immediately verified through implementation and experiments using popular simulation environments.

Issues

Theory of learning with reinforcement
Deep Q-Networks and their implementation
Policy gradient methods
actor-critic architectures
Multi-agent learning
Hierarchical learning with reinforcement
Imitation learning
Simulation environments
Learning stabilization
Optimization of agents
Meta-learning in RL
Implementation of RL systems

Benefits

Upon completion of the training, the participant will be able to design and implement reinforcement learning systems using deep neural networks
The ability to effectively train agents in a variety of simulation environments
Will be able to use advanced techniques to stabilize the learning process in complex decision-making systems
Will master methods for implementing and tuning deep reinforcement learning algorithms
Will learn to design agent architectures suitable for specific tasks and environments
Will gain practical experience in solving problems that occur during training of RL systems
Will learn techniques for optimizing the performance and stability of reinforcement learning
Will be able to implement multi-agent systems and hierarchical learning architectures

Who is this training for?

AI developers interested in learning with reinforcement

Machine learning engineers looking for new areas of development

Data scientists working on advanced decision-making systems

Artificial intelligence researchers

Developers of autonomous systems

Specialists in process optimization

Robotics engineers

AI systems architects

Prerequisites

Practical knowledge of programming in Python
Basic knowledge of neural networks
Understand the basics of machine learning
Knowledge of the basics of probability theory

Training program

Value-based learning algorithms

actor-critic architectures
Deep Reinforcement Learning
Integration of neural networks with RL
Deep Q-Networks and their variants
Policy optimization in deep learning
Learning stabilization techniques

Multi-agent reinforcement learning
Hierarchical learning with reinforcement

Meta-learning in RL
Implementation and applications
Simulation environments and their use
Implementation of learning agents

Implementation of RL systems

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

Who is the Deep Reinforcement Learning with Python training for?

The target audience includes AI developers interested in learning with reinforcement, machine learning engineers looking for new areas of development, and data scientists working on advanced decision-making systems. Level: intermediate.

How long is the Deep Reinforcement Learning with Python training?

The training lasts 3 days. It is delivered online or on-site.

Will I receive a certificate?

Yes — every participant receives a completion certificate confirming acquired competencies. EITT is ISO 9001 certified.

Can this training be conducted for a closed group?

Yes — we offer dedicated closed trainings for companies. We customize the program to your team's needs. Contact us for an individual quote.