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Course Outline
Foundations of Reinforcement Learning and Agentic AI
- Navigating decision-making under uncertainty and sequential planning
- Core RL elements: agents, environments, states, and reward mechanisms
- The function of RL in adaptive and agentic AI architectures
Markov Decision Processes (MDPs)
- Formal definitions and characteristics of MDPs
- Value functions, Bellman equations, and dynamic programming approaches
- Processes for policy evaluation, refinement, and iterative improvement
Model-Free Reinforcement Learning
- Monte Carlo methods and Temporal-Difference (TD) learning
- Q-learning and SARSA algorithms
- Practical application: developing tabular RL methods in Python
Deep Reinforcement Learning
- Merging neural networks with RL for enhanced function approximation
- Deep Q-Networks (DQN) and the concept of experience replay
- Actor-Critic frameworks and policy gradient techniques
- Practical application: training agents with DQN and PPO via Stable-Baselines3
Exploration Tactics and Reward Engineering
- Managing the trade-off between exploration and exploitation (ε-greedy, UCB, entropy-based methods)
- Crafting reward functions and mitigating unintended behavioral outcomes
- Strategies for reward shaping and curriculum-based learning
Advanced Concepts in RL and Decision-Making
- Multi-agent RL and cooperative strategy development
- Hierarchical reinforcement learning and the options framework
- Offline RL and imitation learning for safer deployment scenarios
Simulation Platforms and Assessment
- Leveraging OpenAI Gym and developing custom environments
- Differentiating between continuous and discrete action spaces
- Key metrics for evaluating agent performance, stability, and sample efficiency
Embedding RL into Agentic AI Frameworks
- Synthesizing reasoning and RL in hybrid agent designs
- Integrating RL with tool-using agent capabilities
- Operational strategies for scaling and production deployment
Capstone Project
- Architecting and building an RL agent for a defined simulated task
- Evaluating training outcomes and fine-tuning hyperparameters
- Demonstrating adaptive capabilities and decision-making within an agentic context
Conclusion and Future Directions
Requirements
- Advanced competence in Python programming
- A robust grasp of machine learning and deep learning paradigms
- Familiarity with linear algebra, probability theory, and foundational optimization techniques
Target Audience
- Specialists in reinforcement learning and applied AI research
- Developers focused on robotics and automation systems
- Engineering groups developing adaptive and agentic AI solutions
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
