Python Reinforcement Learning: From Q-Tables to Deep RL
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.
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Reinforcement Learning Models in Python
RL fundamentals: agents, environments, states, actions, rewards, and the exploration-exploitation trade-off.
Python Q-Learning
Tabular Q-learning implementation, Q-table updates, epsilon-greedy exploration, and convergence.
Python Deep Q-Networks (DQN)
Using neural networks as Q-function approximators, experience replay, and target networks.
Python Actor-Critic Methods
Policy gradient methods, advantage estimation, A2C/A3C, and PPO for continuous action spaces.