Reinforcement & Decision-Making for Agentic AI (with Python) Training Course

Introduction to Reinforcement Learning and Agentic AI for Government

• Decision-making under uncertainty and sequential planning in government operations
• Key components of reinforcement learning (RL) for government: agents, environments, states, and rewards
• The role of RL in adaptive and agentic AI systems for government applications

Markov Decision Processes (MDPs) for Government

• Formal definition and properties of MDPs for government use cases
• Value functions, Bellman equations, and dynamic programming in public sector applications
• Policy evaluation, improvement, and iteration in governmental decision-making processes

Model-Free Reinforcement Learning for Government

• Monte Carlo and Temporal-Difference (TD) learning methods for government
• Q-learning and SARSA techniques for public sector applications
• Hands-on: implementing tabular RL methods in Python for government projects

Deep Reinforcement Learning for Government

• Combining neural networks with RL for function approximation in government systems
• Deep Q-Networks (DQN) and experience replay for governmental applications
• Actor-Critic architectures and policy gradients in public sector contexts
• Hands-on: training an agent using DQN and PPO with Stable-Baselines3 for government tasks

Exploration Strategies and Reward Shaping for Government

• Balancing exploration vs. exploitation (ε-greedy, UCB, entropy methods) in governmental decision-making
• Designing reward functions and avoiding unintended behaviors in public sector applications
• Reward shaping and curriculum learning for government use cases

Advanced Topics in RL and Decision-Making for Government

• Multi-agent reinforcement learning and cooperative strategies for governmental operations
• Hierarchical reinforcement learning and options framework for complex public sector tasks
• Offline RL and imitation learning for safer deployment in government systems

Simulation Environments and Evaluation for Government

• Using OpenAI Gym and custom environments for governmental simulations
• Continuous vs. discrete action spaces in public sector applications
• Metrics for agent performance, stability, and sample efficiency in government contexts

Integrating RL into Agentic AI Systems for Government

• Combining reasoning and RL in hybrid agent architectures for governmental use
• Integrating reinforcement learning with tool-using agents for government tasks
• Operational considerations for scaling and deployment of RL systems in the public sector

Capstone Project for Government

• Design and implement a reinforcement learning agent for a simulated governmental task
• Analyze training performance and optimize hyperparameters for government applications
• Demonstrate adaptive behavior and decision-making in an agentic context for public sector use

Summary and Next Steps for Government