ReinforcementLearning

Python Reinforcement Learning: From Q-Tables to Deep RL

4 articles intermediate / advanced

Reinforcement learning trains agents to make decisions by interacting with an environment and learning from rewards. Unlike supervised learning, there are no labeled examples -- the agent discovers optimal behavior through trial, error, and reward signals. This paradigm powers game-playing AI, robotics, recommendation systems, and autonomous decision-making.

This path moves from tabular Q-learning through deep reinforcement learning with neural network function approximators.

RL Algorithms

4 articles

Reinforcement Learning Models in Python

RL fundamentals: agents, environments, states, actions, rewards, and the exploration-exploitation trade-off.

Intermediate read()

Python Q-Learning

Tabular Q-learning implementation, Q-table updates, epsilon-greedy exploration, and convergence.

Intermediate read()

Python Deep Q-Networks (DQN)

Using neural networks as Q-function approximators, experience replay, and target networks.

Advanced read()

Python Actor-Critic Methods

Policy gradient methods, advantage estimation, A2C/A3C, and PPO for continuous action spaces.

Advanced read()