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
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Theory of learning with reinforcement
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Deep Q-Networks and their implementation
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Policy gradient methods
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actor-critic architectures
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Multi-agent learning
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Hierarchical learning with reinforcement
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Imitation learning
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Simulation environments
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Learning stabilization
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Optimization of agents
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Meta-learning in RL
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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?
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
Markov decision process theory
- Value-based learning algorithms
Policy gradient methods
- 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
Advanced techniques
- Multi-agent reinforcement learning
- Hierarchical learning with reinforcement
Imitation learning
- Meta-learning in RL
- Implementation and applications
- Simulation environments and their use
- Implementation of learning agents
Performance optimization
- 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?
This training is designed for professionals looking to develop skills in deep reinforcement learning with python. Required level: intermediate.
How long is the Deep Reinforcement Learning with Python training?
The training lasts 3. Available in online or on-site format.
Will I receive a certificate?
Yes — every participant receives a completion certificate confirming acquired competencies. EITT holds ISO 9001 accreditation.
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.
Request a quote
Funding Options
Check funding options for your company
Development Services Database
Up to 80% funding for SMEs from EU funds
Check availabilityNational Training Fund
Up to 100% funding for employers
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