mokeddembillel/lunar-lander-deep-expected-sarsa

Using deep expected sarsa with tensorflow to solve the lunar lander problem with hyperparameter tuning and results analysis

Lunar-Lander-Deep-Expected-Sarsa

This project is about using deep expected sarsa with tensorflow to solve the lunar lander problem with hyperparameter tuning and result's analysis

These points are important read them please:

• all data and models are in results folder

• folder tests is just for you if you want to test something

• if you want to plot the data, you don't need tensorflow just python and matplotlib

• you need TensorFlow 2.3 for it to work

the code is structured this way:

• imports

• replay buffer class

• expected sarsa network

• softmax and argmax helper functions

• agent class

• lunarlander class

• loading, parsing plotting helper functions

• run experiment function for testing egreedy and softmax

• run experiment function for testing batch size and replay steps

• run experiment normal run

• setting all learning parameters, data and the call for the run experiment function

• cell for loading data and defining the loaded variable (run it before trying to plot or test the agent)

• cell to compute averages

• cell to plot same : plot all data (rewards, loss, episode steps) for each type of tests

• cell for choosing best batch size and number of replay steps

• cell for choosing best softmax tau and step-size

• cell for choosing best e-greedy epsilon and step-size

• INFO CELL (IMPORTANT READ IT PLEASE)

• cell to load and reconstruct model

• cell to plot data of best model

• cell to test the agent

• thank you :)