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Course Outline
Foundations of Reinforcement Learning and Agentic AI
- Decision-making under uncertainty and sequential planning
- Core RL components: agents, environments, states, and rewards
- The role of RL in enabling adaptive and agentic AI systems
Markov Decision Processes (MDPs)
- Formal definitions and characteristics of MDPs
- Value functions, Bellman equations, and dynamic programming
- Processes for policy evaluation, improvement, and iteration
Model-Free Reinforcement Learning
- Monte Carlo and Temporal-Difference (TD) learning methods
- Q-learning and SARSA algorithms
- Practical application: Implementing tabular RL methods in Python
Deep Reinforcement Learning
- Integrating neural networks with RL for function approximation
- Deep Q-Networks (DQN) and experience replay mechanisms
- Actor-Critic architectures and policy gradient methods
- Practical application: Training agents using DQN and PPO via Stable-Baselines3
Exploration Strategies and Reward Shaping
- Managing the exploration vs. exploitation trade-off (ε-greedy, UCB, entropy methods)
- Crafting reward functions and mitigating unintended behaviors
- Techniques for reward shaping and curriculum learning
Advanced Topics 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 enhanced deployment safety
Simulation Environments and Evaluation
- Utilizing OpenAI Gym and building custom environments
- Distinguishing between continuous and discrete action spaces
- Assessing agent performance, stability, and sample efficiency through key metrics
Integrating RL into Agentic AI Systems
- Blending reasoning and RL within hybrid agent architectures
- Embedding reinforcement learning into tool-using agents
- Operational considerations for scaling and production deployment
Capstone Project
- Designing and implementing a reinforcement learning agent for a simulated scenario
- Analyzing training performance and tuning hyperparameters
- Demonstrating adaptive behavior and decision-making within an agentic context
Conclusion and Future Directions
Requirements
- Advanced proficiency in Python programming
- A robust grasp of machine learning and deep learning concepts
- Working knowledge of linear algebra, probability, and fundamental optimization techniques
Target Audience
- Reinforcement learning engineers and applied AI researchers
- Developers specializing in robotics and automation
- Engineering teams focused on 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
