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Course Outline
Foundations of Reinforcement Learning and Agentic AI
- Navigating decision-making under uncertainty and sequential planning
- Core RL components: agents, environments, state spaces, and reward mechanisms
- The strategic role of RL in adaptive and agentic AI architectures
Markov Decision Processes (MDPs)
- Rigorous definition and structural properties of MDPs
- Exploring value functions, Bellman equations, and dynamic programming approaches
- Techniques for policy evaluation, improvement, and iterative refinement
Model-Free Reinforcement Learning
- Comparison of Monte Carlo methods and Temporal-Difference (TD) learning
- Deep dive into Q-learning and SARSA algorithms
- Practical session: Coding tabular RL methods in Python
Deep Reinforcement Learning
- Integrating neural networks with RL for advanced function approximation
- Implementing Deep Q-Networks (DQN) with experience replay mechanisms
- Understanding Actor-Critic architectures and policy gradient methods
- Hands-on workshop: Training agents with DQN and PPO using Stable-Baselines3
Exploration Strategies and Reward Design
- Techniques for balancing exploration and exploitation (including ε-greedy, UCB, and entropy-based methods)
- Crafting effective reward functions to prevent unintended agent behaviors
- Utilizing reward shaping and curriculum learning for structured training
Advanced Concepts in RL and Decision-Making
- Multi-agent reinforcement learning and cooperative strategy formulation
- Applying hierarchical reinforcement learning and the options framework
- Offline RL and imitation learning strategies for safer deployment
Simulation Environments and Performance Evaluation
- Leveraging OpenAI Gym and developing custom simulation environments
- Distinguishing between continuous and discrete action spaces
- Key metrics for assessing agent performance, stability, and sample efficiency
Integrating RL into Agentic AI Systems
- Blending reasoning capabilities with RL in hybrid agent architectures
- Synchronizing reinforcement learning with tool-using agentic workflows
- Operational strategies for scaling and production deployment
Capstone Project
- Designing and building a reinforcement learning agent for a specific simulated task
- Analyzing training outcomes and refining hyperparameters for optimal performance
- Demonstrating adaptive decision-making within an agentic context
Conclusion and Future Directions
Requirements
- Advanced proficiency in Python programming
- A robust understanding of core machine learning and deep learning concepts
- Familiarity with linear algebra, probability theory, and foundational optimization techniques
Target Audience
- Reinforcement learning engineers and applied AI researchers
- Developers specializing in robotics and automation
- Engineering teams developing adaptive and agentic AI systems
28 Hours
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
