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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 characteristics 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 exercise: implementing tabular RL methods in Python
Deep Reinforcement Learning
- Integrating neural networks with RL for function approximation
- Deep Q-Networks (DQN) and experience replay
- Actor-Critic architectures and policy gradients
- Practical exercise: 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 mitigating 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 the options framework
- Offline RL and imitation learning for safer deployment
Simulation Environments and Evaluation
- Utilising OpenAI Gym and custom environments
- Continuous versus discrete action spaces
- Metrics for assessing 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 implementation of a reinforcement learning agent for a simulated task
- Analysis of training performance and hyperparameter optimisation
- Demonstration of adaptive behaviour and decision-making in an agentic context
Summary and Next Steps
Requirements
- Advanced proficiency in Python programming
- A robust understanding of machine learning and deep learning concepts
- Familiarity with linear algebra, probability, and fundamental optimisation methods
Target Audience
- Reinforcement learning engineers and applied AI researchers
- Robotics and automation developers
- 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
