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Course Outline
Foundations of Reinforcement Learning in Agentic AI
- Decision-making processes under uncertainty and sequential planning
- Core RL elements: agents, environments, states, and reward structures
- The role of RL in enabling adaptive and agentic AI systems
Markov Decision Processes (MDPs)
- Formal definitions and key properties of MDPs
- Value functions, Bellman equations, and dynamic programming techniques
- Techniques for policy evaluation, improvement, and iteration
Model-Free Reinforcement Learning
- Monte Carlo methods and Temporal-Difference (TD) learning
- Q-learning and SARSA algorithms
- Practical exercise: Building tabular RL methods in Python
Deep Reinforcement Learning
- Integrating neural networks with RL for function approximation
- Deep Q-Networks (DQN) and the experience replay mechanism
- Actor-Critic architectures and policy gradient methods
- Practical exercise: Training agents using DQN and PPO via Stable-Baselines3
Exploration Tactics and Reward Shaping
- Balancing exploration against exploitation (using ε-greedy, UCB, and entropy-based methods)
- Designing effective reward functions to prevent unintended behaviors
- Applying reward shaping and curriculum learning strategies
Advanced RL and Decision-Making Concepts
- Multi-agent reinforcement learning and cooperative strategies
- Hierarchical reinforcement learning and the options framework
- Offline RL and imitation learning for safer deployment scenarios
Simulation Environments and Performance Evaluation
- Leveraging OpenAI Gym and building custom environments
- Distinguishing between continuous and discrete action spaces
- Metrics for evaluating agent performance, stability, and sample efficiency
Integrating RL into Agentic AI Architectures
- Combining reasoning capabilities with RL in hybrid agent designs
- Integrating reinforcement learning with tool-using agents
- Operational considerations for scaling and production deployment
Capstone Project
- Designing and building a reinforcement learning agent for a simulated scenario
- Analyzing training performance and optimizing hyperparameters
- Demonstrating adaptive behavior and decision-making within an agentic context
Conclusions and Future Directions
Requirements
- Advanced proficiency in Python programming
- Robust understanding 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 systems
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
