Reinforcement Learning in the Open AI Gym

Description

Reinforcement Learning in the Open AI Gym: Code, Experiment, Master

The OpenAI Gym is the playground of reinforcement learning—a standardized toolkit for developing and comparing RL agents across environments like CartPole, MountainCar, LunarLander, and Atari games. This course takes you from first principles to deploying deep RL agents that learn from raw pixels.

What You’ll Build

• A Q-learning agent that solves FrozenLake and Taxi
• A Deep Q-Network (DQN) that plays Atari Pong from screen pixels
• A REINFORCE policy gradient agent for continuous control
• An A2C (Advantage Actor-Critic) agent for CartPole and LunarLander

Key Concepts Covered

• Markov Decision Processes (MDPs)—states, actions, rewards, transitions
• Value-based methods—Q-learning, SARSA, DQN, Double DQN
• Policy-based methods—REINFORCE, actor-critic, A2C
• Exploration vs exploitation—epsilon-greedy, Boltzmann, UCB
• Experience replay & target networks—stabilizing deep RL training

Why This Course?

• 100% hands-on—every lecture ends with working code
• OpenAI Gym mastery—learn to create custom environments and wrappers
• Debugging focus—understand reward shaping, convergence, and instability

Who Is This For?

• ML engineers diving into RL
• Students preparing for RL projects or competitions
• Game AI developers
• Researchers prototyping new algorithms

Your RL Journey Starts in the Gym

This course gives you the **exact workflow** used by top labs and startups to prototype, test, and scale reinforcement learning systems.

Ready to train your first agent? Enroll now.