Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Foundations of Reinforcement Learning and Agentic AI
- Sequential planning and decision-making under uncertainty
- Core components of RL: agents, environments, states, and rewards
- The function of RL in adaptive and agentic AI architectures
Markov Decision Processes (MDPs)
- Rigorous definition and characteristics 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: implementing tabular RL methods in Python
Deep Reinforcement Learning
- Merging 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 session: training agents using DQN and PPO with Stable-Baselines3
Exploration Strategies and Reward Shaping
- Striking a balance between exploration and exploitation (ε-greedy, UCB, entropy-based methods)
- Crafting reward functions and mitigating unintended behaviors
- Techniques for 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 enhanced deployment safety
Simulation Environments and Evaluation
- Leveraging OpenAI Gym and custom-built environments
- Distinguishing between continuous and discrete action spaces
- Metrics for assessing agent performance, stability, and sample efficiency
Integrating RL into Agentic AI Systems
- Synthesizing reasoning and RL within hybrid agent architectures
- Combining reinforcement learning with tool-using agents
- Operational factors regarding scaling and deployment
Capstone Project
- Architecting and implementing a reinforcement learning agent for a simulated task
- Evaluating training performance and refining hyperparameters
- Illustrating adaptive behavior and decision-making within an agentic context
Summary and Future Directions
Requirements
- Advanced proficiency in Python programming
- A strong command of machine learning and deep learning concepts
- Knowledge of 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 building 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
