Course Outline
Introduction to Reinforcement Learning and Agentic AI
- Decision-making under uncertainty and the principles of sequential planning
- Core components of RL: agents, environments, states, and rewards
- The role of RL in fostering adaptive and agentic AI systems
Markov Decision Processes (MDPs)
- Formal definitions and properties of MDPs
- Value functions, Bellman equations, and dynamic programming techniques
- Processes of policy evaluation, improvement, and iteration
Model-Free Reinforcement Learning
- Monte Carlo methods and Temporal-Difference (TD) learning
- Q-learning and SARSA algorithms
- Practical application: implementing tabular RL methods in Python
Deep Reinforcement Learning
- Integrating neural networks with RL for function approximation
- Deep Q-Networks (DQN) and the use of experience replay
- Actor-Critic architectures and policy gradient methods
- Practical application: training agents using DQN and PPO with Stable-Baselines3
Exploration Strategies and Reward Shaping
- Balancing exploration against exploitation (including ε-greedy, UCB, and entropy methods)
- Crafting reward functions and mitigating unintended behaviors
- Techniques for reward shaping and curriculum learning
Advanced Topics in RL and Decision-Making
- Multi-agent reinforcement learning and cooperative strategies
- Hierarchical reinforcement learning and the options framework
- Offline RL and imitation learning for safer deployment scenarios
Simulation Environments and Evaluation
- Leveraging OpenAI Gym and custom-built environments
- Distinguishing between continuous and discrete action spaces
- Metrics for assessing agent performance, stability, and sample efficiency
Integrating RL into Agentic AI Systems
- Blending reasoning and RL within hybrid agent architectures
- Combining reinforcement learning with tool-using agents
- Operational considerations for scaling and deployment
Capstone Project
- Designing and implementing a reinforcement learning agent for a simulated task
- Analyzing training performance and optimizing hyperparameters
- Demonstrating adaptive behavior and decision-making within an agentic context
Summary and Next Steps
Requirements
- Advanced proficiency in Python programming
- A robust understanding of machine learning and deep learning concepts
- Familiarity with linear algebra, probability theory, and fundamental optimization methods
Target Audience
- Reinforcement learning engineers and applied AI researchers
- Developers specializing in robotics and automation
- Engineering teams developing adaptive and agentic AI systems
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 6400 € + VAT*
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Testimonials (3)
The trainer is patient and very helpful. He knows the topic well.
CLIFFORD TABARES - Universal Leaf Philippines, Inc.
Course - Agentic AI for Business Automation: Use Cases & Integration
Good mixvof knowledge and practice
Ion Mironescu - Facultatea S.A.I.A.P.M.
Course - Agentic AI for Enterprise Applications
The mix of theory and practice and of high level and low level perspectives
