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Course Outline
Foundations of Reinforcement Learning and Agentic AI
- Decision-making under uncertainty and sequential planning techniques
- Core RL elements: agents, environments, states, and reward structures
- The function of RL in adaptive and agentic AI ecosystems
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: Coding 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 exercise: Training agents with DQN and PPO via Stable-Baselines3
Exploration Techniques and Reward Shaping
- Managing exploration vs. exploitation (ε-greedy, UCB, entropy methods)
- Crafting reward functions and preventing unintended actions
- Reward shaping strategies and curriculum learning
Advanced Concepts in RL and Decision-Making
- Multi-agent reinforcement learning and cooperative tactics
- Hierarchical reinforcement learning and the options framework
- Offline RL and imitation learning for secure deployment
Simulation Environments and Performance Assessment
- Leveraging OpenAI Gym and custom-built environments
- Differences between continuous and discrete action spaces
- Evaluation metrics for agent performance, stability, and sample efficiency
Embedding RL into Agentic AI Architectures
- Blending reasoning and RL in hybrid agent structures
- Integrating reinforcement learning with tool-using agents
- Operational factors for scaling and production deployment
Capstone Project
- Architect and build a reinforcement learning agent for a simulated objective
- Review training outcomes and refine hyperparameters
- Demonstrate adaptive decision-making within an agentic setting
Conclusion and Future Directions
Requirements
- Advanced proficiency in Python programming
- A robust command of machine learning and deep learning principles
- Working knowledge of linear algebra, probability theory, and fundamental optimization techniques
Target Audience
- Specialists in reinforcement learning and applied AI researchers
- Developers focused on robotics and automation solutions
- 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
