Reinforcement Learning (Q-Learning)
Watch an agent learn to navigate a grid world through trial and error. Rewards and penalties guide the agent to discover optimal paths (policies).
Learn Reinforcement Learning on DataCamp
Curated courses and career tracks to take your understanding from this demo to real-world mastery. All links open directly on DataCamp.
Reinforcement Learning with Gymnasium in Python
Build RL agents using the Gymnasium library. Learn Q-learning, policy gradients, and reward shaping to solve classic control problems.
Deep Reinforcement Learning in Python
Combine deep learning and RL with Deep Q-Networks (DQN), PPO, and Actor-Critic algorithms for complex environments.
Reinforcement Learning from Human Feedback (RLHF)
Learn how RLHF is used to align LLMs like ChatGPT. Understand reward modeling, proximal policy optimization (PPO), and fine-tuning.
Introduction to Deep Learning with PyTorch
Build the neural network foundation needed before implementing Deep Q-Networks and policy gradient methods.
Developing AI Systems with the OpenAI API
Learn how modern RL-trained AI systems are deployed via APIs. Understand prompt engineering and model alignment.
Reinforcement Learning in Python
Master the full reinforcement learning stack—from Q-tables and temporal-difference learning to deep RL and RLHF.
