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 components of RL: agents, environments, states, and rewards.
- The role of RL in adaptive and agentic AI systems.
Markov Decision Processes (MDPs)
- Formal definition and properties of MDPs.
- Value functions, Bellman equations, and dynamic programming.
- Policy evaluation, improvement, and iterative processes.
Model-Free Reinforcement Learning
- Monte Carlo and Temporal-Difference (TD) learning.
- Q-learning and SARSA algorithms.
- Hands-on: Implementing tabular RL methods in Python.
Deep Reinforcement Learning
- Integrating neural networks with RL for function approximation.
- Deep Q-Networks (DQN) and experience replay techniques.
- Actor-Critic architectures and policy gradients.
- Hands-on: Training an agent using DQN and PPO with Stable-Baselines3.
Exploration Strategies and Reward Shaping
- Managing the trade-off between exploration and exploitation (e.g., ε-greedy, UCB, entropy methods).
- Designing effective reward functions while preventing unintended behaviors.
- Reward shaping and curriculum learning approaches.
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 safety in deployment.
Simulation Environments and Evaluation
- Leveraging OpenAI Gym and custom environments.
- Distinctions between continuous and discrete action spaces.
- Metrics for assessing agent performance, stability, and sample efficiency.
Integrating RL into Agentic AI Systems
- Merging reasoning capabilities with RL in hybrid agent architectures.
- Integrating reinforcement learning with tool-using agents.
- Operational considerations for scaling and deployment.
Capstone Project
- Design and implement a reinforcement learning agent for a simulated task.
- Analyze training performance and optimize hyperparameters.
- Demonstrate adaptive behavior and decision-making within an agentic context.
Summary and Next Steps
Requirements
- Advanced proficiency in Python programming.
- A solid grasp of machine learning and deep learning concepts.
- Familiarity with linear algebra, probability theory, and fundamental optimization methods.
Target Audience
- Reinforcement learning engineers and applied AI researchers.
- Developers specializing in robotics and automation.
- Engineering teams focused on 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
