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Course Outline
Introduction to Reinforcement Learning
- Defining reinforcement learning.
- Key concepts: agent, environment, states, actions, and rewards.
- Challenges inherent in reinforcement learning.
Exploration and Exploitation
- Balancing exploration and exploitation within RL models.
- Exploration strategies: epsilon-greedy, softmax, and others.
Q-Learning and Deep Q-Networks (DQNs)
- Overview of Q-learning.
- Implementing DQNs using TensorFlow.
- Optimizing Q-learning via experience replay and target networks.
Policy-Based Methods
- Policy gradient algorithms.
- The REINFORCE algorithm and its implementation.
- Actor-critic methods.
Working with OpenAI Gym
- Configuring environments in OpenAI Gym.
- Simulating agents within dynamic environments.
- Assessing agent performance.
Advanced Reinforcement Learning Techniques
- Multi-agent reinforcement learning.
- Deep deterministic policy gradient (DDPG).
- Proximal policy optimization (PPO).
Deploying Reinforcement Learning Models
- Real-world applications of reinforcement learning.
- Integrating RL models into production environments.
Summary and Next Steps
Requirements
- Proficiency in Python programming.
- Foundational knowledge of deep learning and machine learning concepts.
- Familiarity with the algorithms and mathematical principles integral to reinforcement learning.
Audience
- Data scientists.
- Machine learning practitioners.
- AI researchers.
28 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 5200 € + VAT*
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