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Course Outline
Introduction to Reinforcement Learning and Agentic AI
- Navigating decision-making under uncertainty and sequential planning.
- Core components of RL: agents, environments, states, and rewards.
- The significance of RL in adaptive and agentic AI systems.
Markov Decision Processes (MDPs)
- Formal definitions and properties of MDPs.
- Value functions, Bellman equations, and dynamic programming.
- Policy evaluation, improvement, and iterative processes.
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
- Integrating neural networks with RL for function approximation.
- Deep Q-Networks (DQN) and experience replay mechanisms.
- 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 versus exploitation (ε-greedy, UCB, entropy methods).
- Designing effective reward functions while avoiding unintended behaviors.
- Reward shaping and curriculum learning techniques.
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 safety in deployment.
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
- Combining reasoning capabilities with RL in hybrid agent architectures.
- Incorporating reinforcement learning into tool-using agents.
- Operational considerations for scaling and deploying systems.
Capstone Project
- Designing and implementing a reinforcement learning agent for a simulated task.
- Analyzing training performance and optimizing hyperparameters.
- Demonstrating adaptive behavior and decision-making within an agentic context.
Summary and Next Steps
Requirements
- Proficiency in Python programming.
- A solid grasp of machine learning and deep learning principles.
- Familiarity with linear algebra, probability theory, and fundamental optimization techniques.
Audience
- Reinforcement learning engineers and applied AI researchers.
- Developers specializing in robotics and automation.
- Engineering teams developing 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
