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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
- 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
- Hands-on: 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
- Hands-on: training an agent using DQN and PPO with Stable-Baselines3
Exploration Strategies and Reward Shaping
- Balancing exploration vs. exploitation (ε-greedy, UCB, entropy methods)
- Designing reward functions and avoiding unintended behaviors
- 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 vs. 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
- Analyze training performance and optimize hyperparameters
- Demonstrate adaptive behavior and decision-making in an agentic context
Summary and Next Steps
Requirements
- Strong proficiency in Python programming
- Solid understanding of machine learning and deep learning concepts
- Familiarity with linear algebra, probability, and basic optimization methods
Audience
- Reinforcement learning engineers and applied AI researchers
- Robotics and automation developers
- Engineering teams working on adaptive and agentic AI systems
28 Hours
Delivery Options
Private Group Training
Our identity is rooted in delivering exactly what our clients need.
- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from £7600 online delivery, based on a group of 2 delegates, £2400 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
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Public Training
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Testimonials (3)
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
Ion Mironescu - Facultatea S.A.I.A.P.M.
Course - Autonomous Decision-Making with Agentic AI
practical exercises
