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Course Outline
Foundations of Reinforcement Learning and Agentic AI
- Decision-making under uncertainty and sequential planning
- Core RL elements: agents, environments, states, and reward signals
- 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
- Techniques for policy evaluation, enhancement, and iteration
Model-Free Reinforcement Learning
- Monte Carlo methods and Temporal-Difference (TD) learning
- Algorithms such as Q-learning and SARSA
- Practical lab: 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 lab: Training agents using DQN and PPO with Stable-Baselines3
Exploration Strategies and Reward Shaping
- Balancing exploration versus exploitation (epsilon-greedy, UCB, entropy-based approaches)
- Crafting reward functions to prevent unintended behaviors
- Implementing reward shaping and curriculum learning
Advanced RL and Decision-Making Concepts
- Multi-agent reinforcement learning and collaborative strategies
- Hierarchical RL and the options framework
- Offline RL and imitation learning for secure deployment
Simulation Environments and Evaluation
- Utilizing OpenAI Gym and custom-built environments
- Distinguishing continuous from discrete action spaces
- Key metrics for assessing agent performance, stability, and sample efficiency
Embedding RL into Agentic AI Systems
- Merging reasoning with RL in hybrid agent designs
- Incorporating RL into tool-using agents
- Operational factors for scaling and production deployment
Capstone Project
- Designing and building an RL agent for a specific simulated task
- Evaluating training results and fine-tuning hyperparameters
- Demonstrating adaptive decision-making within an agentic scenario
Wrap-up and Future Directions
Requirements
- Advanced proficiency in Python programming
- A robust grasp of machine learning and deep learning principles
- Working knowledge of linear algebra, probability theory, and fundamental optimization techniques
Target Audience
- RL engineers and applied AI researchers
- Developers specializing in robotics and automation
- Engineering teams developing adaptive and agentic AI solutions
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
