Reinforcement Learning Simulations

Reinforcement Learning (RL) is a branch of machine learning that focuses on how agents ought to take actions in an environment to maximize some notion of cumulative reward. This page explores various simulations related to reinforcement learning.

Key Concepts

• Agent: The decision-making entity in the environment.
• Environment: The context in which the agent operates.
• State: The current situation or context of the environment.
• Action: The decision made by the agent.
• Reward: The feedback received by the agent after taking an action.

Popular Simulations

1. CartPole: A classic control problem where the goal is to keep a pole balanced on a cart for as long as possible.
2. Mountain Car: The agent must move a car up a mountain by pressing the gas or brake.
3. Lunar Lander: The agent must land a lunar module on the moon's surface without crashing.

Resources

For more information on reinforcement learning simulations, check out our Reinforcement Learning Basics guide.

CartPole Simulation

Mountain Car Simulation

Lunar Lander Simulation