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Course Outline
Foundations of Reinforcement Learning and Agentic AI
- Strategic decision-making under uncertainty and sequential planning
- Core RL elements: agents, environments, states, and reward mechanisms
- The contribution of RL to adaptive and agentic AI architectures
Markov Decision Processes (MDPs)
- Formal definitions and characteristics of MDPs
- Value functions, Bellman equations, and dynamic programming approaches
- Processes for policy evaluation, improvement, and iteration
Model-Free Reinforcement Learning
- Monte Carlo methods and Temporal-Difference (TD) learning
- Q-learning and SARSA algorithms
- Practical exercise: Building tabular RL solutions in Python
Deep Reinforcement Learning
- Integrating neural networks with RL for advanced function approximation
- Deep Q-Networks (DQN) and experience replay techniques
- Actor-Critic structures and policy gradient methods
- Practical exercise: Training agents with DQN and PPO using Stable-Baselines3
Exploration Tactics and Reward Optimization
- Managing the trade-off between exploration and exploitation (ε-greedy, UCB, entropy-based methods)
- Crafting effective reward functions and preventing unexpected behaviors
- Strategies for reward shaping and curriculum learning
Advanced RL and Decision-Making Concepts
- Multi-agent reinforcement learning and cooperative strategies
- Hierarchical reinforcement learning and the options framework
- Offline RL and imitation learning for enhanced safety and deployment
Simulation Platforms and Performance Assessment
- Leveraging OpenAI Gym and building custom environments
- Distinguishing between continuous and discrete action spaces
- Evaluating agent performance, stability, and sample efficiency metrics
Embedding RL into Agentic AI Frameworks
- Fusing reasoning capabilities with RL in hybrid agent designs
- Incorporating reinforcement learning into tool-using agents
- Operational strategies for scaling and production deployment
Capstone Project
- Designing and building a reinforcement learning agent for a simulated scenario
- Analyzing training results and refining hyperparameters
- Demonstrating adaptive decision-making within an agentic setting
Conclusion and Future Directions
Requirements
- Advanced proficiency in Python programming
- A robust grasp of machine learning and deep learning principles
- Knowledge of linear algebra, probability theory, and fundamental optimization techniques
Target Audience
- Reinforcement learning specialists and applied AI researchers
- Developers 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
