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Course Outline
Foundations of Reinforcement Learning and Agentic AI
- Decision-making under uncertainty and the principles of sequential planning
- Essential RL components: agents, environments, states, and reward structures
- The significance of RL in building adaptive and agentic AI systems
Markov Decision Processes (MDPs)
- Formal definitions and characteristics of MDPs
- Value functions, Bellman equations, and dynamic programming techniques
- 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: implementing tabular RL methods in Python
Deep Reinforcement Learning
- Integrating neural networks with RL for function approximation
- Deep Q-Networks (DQN) and the concept of experience replay
- Actor-Critic architectures and policy gradient methods
- Practical exercise: training agents using DQN and PPO via Stable-Baselines3
Exploration Techniques and Reward Shaping
- Strategies for balancing exploration and exploitation (e.g., ε-greedy, UCB, entropy methods)
- Crafting reward functions and preventing unintended agent behaviors
- Reward shaping techniques 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 safer deployment scenarios
Simulation Environments and Assessment
- Utilizing OpenAI Gym and creating custom environments
- Distinguishing between continuous and discrete action spaces
- Metrics for evaluating agent performance, stability, and sample efficiency
Embedding RL in Agentic AI Architectures
- Blending reasoning and RL within hybrid agent structures
- Integrating reinforcement learning with tool-using agents
- Operational factors to consider for scaling and deployment
Capstone Project
- Designing and coding a reinforcement learning agent for a simulated challenge
- Evaluating training results and refining hyperparameters
- Demonstrating adaptive behavior and decision-making within an agentic framework
Wrap-up and Future Directions
Requirements
- Advanced competency in Python programming
- A robust command 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 focused on 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
