Course Outline
Overview of Reinforcement Learning and Agentic Artificial Intelligence
- Strategies for decision-making under uncertainty and sequential planning processes
- Core elements of reinforcement learning, including agents, environments, states, and reward mechanisms
- The function of reinforcement learning in developing adaptive and autonomous AI systems for government applications
Markov Decision Processes (MDPs)
- Formal definitions and mathematical properties of MDPs
- Value functions, Bellman equations, and dynamic programming techniques
- Procedures for policy evaluation, improvement, and iterative optimization
Model-Free Reinforcement Learning
- Monte Carlo methods and Temporal-Difference (TD) learning algorithms
- Q-learning and SARSA algorithmic frameworks
- Practical implementation of tabular reinforcement learning methods using Python for government use cases
Deep Reinforcement Learning
- Integration of neural networks with reinforcement learning for function approximation
- Deep Q-Networks (DQN) and experience replay buffers
- Actor-Critic architectures and policy gradient methods
- Practical exercise: training an agent using DQN and Proximal Policy Optimization (PPO) with Stable-Baselines3 for government scenarios
Exploration Strategies and Reward Shaping
- Balancing exploration versus exploitation, including ε-greedy, Upper Confidence Bound (UCB), and entropy-based methods
- Designing effective reward functions to mitigate unintended agent behaviors
- Reward shaping techniques and curriculum learning approaches
Advanced Topics in Reinforcement Learning and Decision-Making
- Multi-agent reinforcement learning and cooperative strategy development
- Hierarchical reinforcement learning and the options framework
- Offline reinforcement learning and imitation learning for secure deployment in government environments
Simulation Environments and Performance Evaluation
- Utilization of OpenAI Gym and custom-built simulation environments
- Distinctions between continuous and discrete action spaces
- Metrics for assessing agent performance, stability, and sample efficiency
Integration of Reinforcement Learning into Agentic AI Systems
- Combining logical reasoning with reinforcement learning in hybrid agent architectures
- Incorporating reinforcement learning capabilities into tool-using agents for government operations
- Operational considerations regarding scalability and deployment readiness
Capstone Project
- Design and implementation of a reinforcement learning agent for a simulated mission task
- Analysis of training performance and optimization of hyperparameters
- Demonstration of adaptive behavior and decision-making capabilities within an agentic framework
Summary and Future Directions
Requirements
- Demonstrated expertise in Python development
- Comprehensive knowledge of machine learning and deep learning principles
- Working proficiency in linear algebra, probability theory, and fundamental optimization techniques
Target Audience
- Professionals specializing in reinforcement learning and applied artificial intelligence research
- Developers focused on robotics and automated systems
- Engineering groups developing adaptive and autonomous AI solutions for government operations
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
