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Course Outline
Foundations of Reinforcement Learning and Agentic AI
- Decision-making in uncertain environments and sequential planning
- Core elements of RL: agents, environments, states, and reward signals
- The role of RL in enhancing adaptive and agentic AI systems
Markov Decision Processes (MDPs)
- Formal definitions and properties of MDPs
- Value functions, Bellman equations, and dynamic programming methods
- Processes for policy evaluation, improvement, and iteration
Model-Free Reinforcement Learning
- Monte Carlo methods and Temporal-Difference (TD) learning
- Overview of Q-learning and SARSA algorithms
- Practical implementation of tabular RL methods in Python
Deep Reinforcement Learning
- Integrating neural networks with RL for value function approximation
- Deep Q-Networks (DQN) and the concept of experience replay
- Actor-Critic architectures and policy gradient methods
- Hands-on session: training agents with DQN and PPO using Stable-Baselines3
Exploration Strategies and Reward Shaping
- Techniques for balancing exploration vs. exploitation (e.g., ε-greedy, UCB, entropy regularization)
- Strategies for designing effective reward functions and preventing unintended behaviors
- Applications of reward shaping and curriculum learning
Advanced Concepts 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 system deployment
Simulation Environments and Evaluation Metrics
- Utilizing OpenAI Gym and building 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 Architectures
- Blending reasoning capabilities with RL in hybrid agent architectures
- Combining reinforcement learning with tool-using agent capabilities
- Operational considerations for scaling systems and production deployment
Capstone Project
- Design and implementation of a reinforcement learning agent for a specific simulated task
- Analysis of training performance and hyperparameter optimization
- Demonstration of adaptive behavior and decision-making within an agentic context
Course Conclusion and Future Directions
Requirements
- Advanced proficiency in Python programming
- A robust understanding of machine learning and deep learning concepts
- Knowledge of linear algebra, probability theory, and fundamental 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
