Part of SHKYERA project

Rocket Landing - Reinforcement Learning

Environment

I made a custom OpenAI-Gym environment with fully functioning 2D physics engine. If you want to test your own algorithms using that, download the package by simply typing in terminal:

pip install rocketgym

All the environment's functionalities are described here.

Minimal usage

Make sure that all dependencies are installed by pip install -r requirements.txt

from rocketgym.environment import Environment

import random

env = Environment()
observation = env.reset()
done = False

while not done:
    observation, reward, done, info = env.step(random.randint(0,3))
    env.render()

Learning

python3 train.py -h
usage: Rocket Landing - Reinforcemeng Learning [-h] [--curriculum] [--softmax] [--save] [-model MODEL]

optional arguments:
  -h, --help    show this help message and exit
  --curriculum  Use Curriculum Learning
  --softmax     Use Softmax exploration instead of eps-greedy
  --save        Save flight logs and models every 100 episodes
  -model MODEL  Path to the model to load. Overrides the curriculum and exploration
                settings. Renders the scene from the start.

In the train.py you can see, how agent training is implemented. All you need to do is specify the exploration strategy and adjust the environment to your needs. I found that it takes around 2000 iterations to learn to land without any curriculum learning, but the process can be significantly sped up by setting up a task difficulty schedule. This can be easily done through the Curriculum module.

Diagnostics

If you want to make pretty plots, like this one feel free to use diagnostics.py. All you need to know to do that is described in the script itself.

For a detailed explanation of the environment and the learning algorithms I used, see here.