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Course Outline
Introduction to Reinforcement Learning and Agentic AI for Government
- 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 for Government
- 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 for government applications
- Solid understanding of machine learning and deep learning concepts, aligned with public sector workflows
- Familiarity with linear algebra, probability, and basic optimization methods to support governance and accountability
Audience
- Reinforcement learning engineers and applied AI researchers for government projects
- Robotics and automation developers working on government initiatives
- Engineering teams focused on adaptive and agentic AI systems for government use
28 Hours
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
