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
Introduction to Reinforcement Learning and Agentic AI
- Decision-making under uncertainty and sequential planning
- Core elements of RL: agents, environments, states, and rewards
- The function of RL in adaptive and agentic AI systems
Markov Decision Processes (MDPs)
- Formal definitions and key properties of MDPs
- Value functions, Bellman equations, and dynamic programming
- 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: 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 exercise: training agents with DQN and PPO using Stable-Baselines3
Exploration Strategies and Reward Shaping
- Managing the balance between exploration and exploitation (ε-greedy, UCB, entropy methods)
- Crafting reward functions to prevent unintended behaviors
- Techniques for reward shaping and curriculum learning
Advanced Topics in RL and Decision-Making
- Multi-agent reinforcement learning and collaborative strategies
- Hierarchical reinforcement learning and the options framework
- Offline RL and imitation learning for safer deployment scenarios
Simulation Environments and Evaluation
- Leveraging OpenAI Gym and custom-built environments
- Differences between continuous and discrete action spaces
- Metric frameworks for assessing agent performance, stability, and sample efficiency
Integrating RL into Agentic AI Systems
- Merging reasoning capabilities with RL in hybrid agent architectures
- Incorporating reinforcement learning into agents that utilize external tools
- Operational considerations for scaling systems and production deployment
Capstone Project
- Designing and building a reinforcement learning agent for a specific simulated task
- Analyzing training outcomes and tuning hyperparameters
- Demonstrating adaptive behavior and decision-making in an agentic setting
Summary and Next Steps
Requirements
- Advanced proficiency in Python programming
- A robust understanding of machine learning and deep learning concepts
- Comfort with 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
