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Duration 28 hours
Course Outline
Foundations of Reinforcement Learning in Agentic AI
- Navigating decision-making processes under uncertainty and sequential planning
- Core RL elements: agents, environments, state spaces, and reward structures
- The strategic role of RL within adaptive and agentic AI frameworks
Markov Decision Processes (MDPs)
- Technical definitions and fundamental properties of MDPs
- Exploring value functions, Bellman equations, and dynamic programming techniques
- Methods for policy evaluation, iterative improvement, and optimization
Model-Free Reinforcement Learning Approaches
- Techniques involving Monte Carlo methods and Temporal-Difference (TD) learning
- Deep dives into Q-learning and SARSA algorithms
- Practical application: Coding tabular RL methods using Python
Deep Reinforcement Learning Architectures
- Leveraging neural networks for function approximation in RL
- Deep Q-Networks (DQN) and the concept of experience replay
- Actor-Critic frameworks and policy gradient mechanisms
- Hands-on session: Training agents with DQN and PPO via Stable-Baselines3
Exploration Strategies and Reward Design
- Managing the trade-off between exploration and exploitation (\u03b5-greedy, UCB, entropy-based methods)
- Formulating reward functions to prevent unintended agent behaviors
- Implementing reward shaping and curriculum learning strategies
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 scenarios
Simulation Environments and Performance Evaluation
- Utilizing OpenAI Gym and developing custom simulation environments
- Differentiating between continuous and discrete action spaces
- Key metrics for assessing agent performance, stability, and sample efficiency
Integrating RL into Agentic AI Systems
- Blending reasoning capabilities with RL in hybrid agent architectures
- Enhancing tool-using agents through reinforcement learning integration
- Operational strategies for scaling and production deployment
Capstone Project
- Building a reinforcement learning agent for a specific simulated task
- Evaluating training outcomes and tuning hyperparameters for optimization
- Demonstrating adaptive decision-making within an agentic context
Recap and Future Directions
Requirements
- Expert-level proficiency in Python programming
- Robust knowledge of machine learning and deep learning principles
- Working 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
- Engineering teams developing adaptive and agentic AI solutions
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
