Reinforcement Learning (RL) and AI Agents

This pathway explains the core components of RL (agent, environment, reward), fundamental algorithms (Q-learning, Policy Gradients), and real-world applications in robotics and dynamic resource management.

After completing this Pathway, you will be able to:

• Formulate a real-world problem as a Markov Decision Process (MDP) and change core RL algorithms (e.g., Q-learning, Policy Gradients) to train an agent
• Design a reward function to guide an agent towards desired behavior