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.
- Key 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 iteration.
Model-Free Reinforcement Learning
- Monte Carlo and Temporal-Difference (TD) learning.
- Q-learning and SARSA.
- Practical: Implementing tabular RL methods in Python.
Deep Reinforcement Learning
- Combining neural networks with RL for function approximation.
- Deep Q-Networks (DQN) and experience replay.
- Actor-Critic architectures and policy gradients.
- Practical: Training an agent using DQN and PPO with Stable-Baselines3.
Exploration Strategies and Reward Shaping
- Balancing exploration versus exploitation (ε-greedy, UCB, entropy methods).
- Designing reward functions and avoiding unintended behaviours.
- Reward shaping and curriculum learning.
Advanced Topics in RL and Decision-Making
- Multi-agent reinforcement learning and cooperative strategies.
- Hierarchical reinforcement learning and options framework.
- Offline RL and imitation learning for safer deployment.
Simulation Environments and Evaluation
- Using OpenAI Gym and custom environments.
- Continuous versus discrete action spaces.
- Metrics for agent performance, stability, and sample efficiency.
Integrating RL into Agentic AI Systems
- Combining reasoning and 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.
- Analyse training performance and optimize hyperparameters.
- Demonstrate adaptive behaviour and decision-making within an agentic context.
Summary and Next Steps
Requirements
- Proficiency in Python programming.
- A solid understanding of machine learning and deep learning concepts.
- Familiarity with linear algebra, probability theory, and basic optimization methods.
Audience
- Reinforcement learning engineers and applied AI researchers.
- Developers specialising in robotics and automation.
- Engineering teams working 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
