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Course Outline
Introduction to Reinforcement Learning and Agentic AI
- Decision-making under uncertainty and sequential planning.
- Key components of RL: agents, environments, states, and rewards.
- The role of RL in adaptive and agentic AI systems.
Markov Decision Processes (MDPs)
- Formal definition and properties of MDPs.
- Value functions, Bellman equations, and dynamic programming.
- Policy evaluation, improvement, and iteration.
Model-Free Reinforcement Learning
- Monte Carlo and Temporal-Difference (TD) learning.
- Q-learning and SARSA.
- Hands-on: implementing tabular RL methods in Python.
Deep Reinforcement Learning
- Combining neural networks with RL for function approximation.
- Deep Q-Networks (DQN) and experience replay.
- Actor-Critic architectures and policy gradients.
- Hands-on: training an agent using DQN and PPO with Stable-Baselines3.
Exploration Strategies and Reward Shaping
- Balancing exploration vs. exploitation (ε-greedy, UCB, entropy methods).
- Designing reward functions and avoiding unintended behaviours.
- Reward shaping and curriculum learning.
Advanced Topics in RL and Decision-Making
- Multi-agent reinforcement learning and cooperative strategies.
- Hierarchical reinforcement learning and options framework.
- Offline RL and imitation learning for safer deployment.
Simulation Environments and Evaluation
- Using OpenAI Gym and custom environments.
- Continuous vs. discrete action spaces.
- Metrics for agent performance, stability, and sample efficiency.
Integrating RL into Agentic AI Systems
- Combining reasoning and RL in hybrid agent architectures.
- Integrating reinforcement learning with tool-using agents.
- Operational considerations for scaling and deployment.
Capstone Project
- Design and implement a reinforcement learning agent for a simulated task.
- Analyse training performance and optimise hyperparameters.
- Demonstrate adaptive behaviour and decision-making in an agentic context.
Summary and Next Steps
Requirements
- Strong proficiency in Python programming.
- Solid understanding of machine learning and deep learning concepts.
- Familiarity with linear algebra, probability, and basic optimisation methods.
Audience
- Reinforcement learning engineers and applied AI researchers.
- Robotics and automation developers.
- Engineering teams working on 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
