Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Foundations of Reinforcement Learning and Agentic AI
- Navigating decision-making under uncertainty and sequential planning
- Core RL elements: agents, environments, states, and reward structures
- The function of RL within adaptive and agentic AI frameworks
Markov Decision Processes (MDPs)
- Formal definitions and key properties of MDPs
- Value functions, Bellman equations, and dynamic programming approaches
- Cycles of policy evaluation, improvement, and iteration
Model-Free Reinforcement Learning
- Monte Carlo methods and Temporal-Difference (TD) learning
- Q-learning and SARSA algorithms
- Practical application: coding 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 application: training agents with DQN and PPO via Stable-Baselines3
Exploration Strategies and Reward Shaping
- Striking a balance between exploration and exploitation (ε-greedy, UCB, entropy techniques)
- Crafting reward functions to prevent unintended agent behaviors
- Techniques for 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 deployment contexts
Simulation Environments and Assessment
- Leveraging OpenAI Gym and building custom environments
- Distinguishing between continuous and discrete action spaces
- Evaluating agent performance, stability, and sample efficiency
Incorporating RL into Agentic AI Systems
- Fusing reasoning and RL within hybrid agent architectures
- Connecting reinforcement learning with tool-utilizing agents
- Operational strategies for scaling and deployment
Capstone Project
- Designing and building a reinforcement learning agent for a simulated objective
- Reviewing training performance and tuning hyperparameters
- Demonstrating adaptive decision-making within an agentic context
Conclusion and Future Directions
Requirements
- Advanced proficiency in Python programming
- Robust knowledge of machine learning and deep learning principles
- Comfort with linear algebra, probability, and basic 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
