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Course Outline
Foundations of Reinforcement Learning and Agentic AI
- Decision-making in uncertain environments and sequential planning
- Essential elements of RL: agents, environments, states, and rewards
- The function of RL within adaptive and agentic AI frameworks
Markov Decision Processes (MDPs)
- Formal characteristics and definitions of MDPs
- Value functions, Bellman equations, and dynamic programming approaches
- Processes for policy evaluation, improvement, and iteration
Model-Free Reinforcement Learning
- Monte Carlo methods and Temporal-Difference (TD) learning
- Q-learning and SARSA algorithms
- Practical session: coding tabular RL methods in Python
Deep Reinforcement Learning
- Integrating neural networks with RL for function approximation
- Deep Q-Networks (DQN) and the concept of experience replay
- Actor-Critic structures and policy gradient methods
- Practical session: training agents using DQN and PPO with Stable-Baselines3
Exploration Methods and Reward Design
- Managing the trade-off between exploration and exploitation (ε-greedy, UCB, entropy-based methods)
- Crafting reward functions and preventing unintended behaviors
- Reward shaping techniques 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 secure deployment
Simulated Environments and Assessment
- Utilizing OpenAI Gym and creating custom environments
- Differences between continuous and discrete action spaces
- Key metrics for assessing agent performance, stability, and sample efficiency
Integrating RL into Agentic AI Architectures
- Merging reasoning capabilities with RL in hybrid agent designs
- Combining reinforcement learning with tool-using agents
- Operational factors for scaling and deployment
Capstone Project
- Design and build a reinforcement learning agent for a simulated scenario
- Evaluate training outcomes and refine hyperparameters
- Demonstrate adaptive behavior and decision-making within an agentic context
Conclusion and Future Directions
Requirements
- High proficiency in Python programming
- A robust grasp of machine learning and deep learning principles
- Experience with linear algebra, probability, and fundamental optimization techniques
Target Audience
- Reinforcement learning engineers and applied AI researchers
- Developers specializing in robotics and automation
- Engineering teams focused on adaptive and agentic AI architectures
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
