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Course Outline
Foundations of Reinforcement Learning and Agentic AI
- Managing uncertainty and sequential planning in decision-making
- Core RL components: agents, environments, states, and reward signals
- The contribution of RL to adaptive and agentic AI systems
Markov Decision Processes (MDPs)
- Defining the structure and properties of MDPs
- Exploring value functions, Bellman equations, and dynamic programming
- Techniques for policy evaluation, improvement, and iterative refinement
Model-Free Reinforcement Learning
- Monte Carlo methods and Temporal-Difference (TD) learning
- Q-learning and SARSA algorithms
- Practical implementation of tabular RL methods in Python
Deep Reinforcement Learning
- Integrating neural networks with RL for function approximation
- Deep Q-Networks (DQN) and the use of experience replay
- Actor-Critic architectures and policy gradient methods
- Practical exercise: training agents using DQN and PPO with Stable-Baselines3
Exploration Strategies and Reward Shaping
- Managing the trade-off between exploration and exploitation (ε-greedy, UCB, entropy methods)
- Crafting reward functions to prevent unintended behaviors
- Applying reward shaping and curriculum learning
Advanced Topics in RL and Decision-Making
- 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 Evaluation
- Utilizing OpenAI Gym and building custom environments
- Distinguishing between continuous and discrete action spaces
- Metric-driven evaluation of agent performance, stability, and sample efficiency
Integrating RL into Agentic AI Systems
- Fusing reasoning with RL in hybrid agent architectures
- Connecting reinforcement learning with tool-using agents
- Operational strategies for scaling and deployment
Capstone Project
- Designing and building a reinforcement learning agent for a specific simulated task
- Evaluating training outcomes and tuning hyperparameters
- Demonstrating adaptive decision-making within an agentic context
Conclusion and Future Directions
Requirements
- Advanced proficiency in Python programming
- A robust understanding of machine learning and deep learning concepts
- Knowledge of linear algebra, probability theory, and fundamental optimization methods
Target Audience
- Reinforcement learning engineers and applied AI researchers
- Developers in robotics and automation
- Engineering teams specializing in 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
