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).
Training Metrics
The agent uses Q-learning to navigate to the Goal (green) while avoiding the Pit (red) and Obstacles (black). Arrows show the learned policy; brighter arrows = higher confidence. Once the greedy path reaches the goal and the last 5 episodes succeed, a solid line marks the discovered shortest route.
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.
