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Course Outline
Foundations of Reinforcement Learning and Agentic AI
- Sequential planning and decision-making under uncertainty
- Essential RL elements: agents, environments, states, and reward structures
- The function of RL in enhancing adaptive and agentic AI systems
Markov Decision Processes (MDPs)
- Formal definitions and inherent properties of MDPs
- Value functions, Bellman equations, and dynamic programming approaches
- Processes for policy evaluation, enhancement, and iterative refinement
Model-Free Reinforcement Learning
- Monte Carlo methods and Temporal-Difference (TD) learning techniques
- Q-learning and SARSA algorithms
- Practical exercise: building tabular RL methods in Python
Deep Reinforcement Learning
- Integrating neural networks with RL for effective function approximation
- Deep Q-Networks (DQN) and the use of experience replay
- Actor-Critic frameworks and policy gradient methods
- Practical exercise: training agents with DQN and PPO using Stable-Baselines3
Exploration Tactics and Reward Shaping
- Navigating the exploration-exploitation dilemma (using ε-greedy, UCB, and entropy techniques)
- Crafting reward functions to prevent unexpected behaviors
- Strategies for reward shaping and curriculum learning
Advanced Concepts in RL and Decision-Making
- Multi-agent reinforcement learning and collaborative strategies
- Hierarchical reinforcement learning and the options framework
- Offline RL and imitation learning for safer implementation
Simulation Environments and Evaluation Metrics
- Leveraging OpenAI Gym and building custom environments
- Distinguishing between continuous and discrete action spaces
- Key metrics for assessing agent performance, stability, and sample efficiency
Embedding RL into Agentic AI Systems
- Fusing reasoning capabilities with RL in hybrid agent designs
- Combining reinforcement learning with tool-utilizing agents
- Operational aspects of scaling and production deployment
Capstone Project
- Designing and coding a reinforcement learning agent for a specific simulated task
- Evaluating training outcomes and tuning hyperparameters
- Demonstrating adaptive decision-making within an agentic framework
Conclusion and Future Directions
Requirements
- Advanced command of Python programming
- A firm grasp of machine learning and deep learning principles
- Knowledge of linear algebra, probability theory, and fundamental optimization techniques
Target Audience
- Reinforcement learning engineers and applied AI researchers
- Developers specializing in robotics and automation
- Engineering teams 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
