Mattiatore / Lunar-Lander

In this project we teach an agent to play the Lunar Lander game from OpenAI Gym. The agent needs to learn how to land a lunar module safely on the surface of the moon. The state space is 8-dimensional and (mostly) continuous, consisting of the X and Y coordinates, the X and Y velocity, the angle, and the angular velocity of the lander, and two booleans indicating whether the left and right leg of the lander have landed on the moon. We will use Policy Gradient approaches (using the REINFORCE rule) to learn the task.

Lunar-Lander

In this project we teach an agent to play the Lunar Lander game from OpenAI Gym. The agent needs to learn how to land a lunar module safely on the surface of the moon. The state space is 8-dimensional and (mostly) continuous, consisting of the X and Y coordinates, the X and Y velocity, the angle, and the angular velocity of the lander, and two booleans indicating whether the left and right leg of the lander have landed on the moon. We will use Policy Gradient approaches (using the REINFORCE rule) to learn the task.