This projects contains an integration of the Super Mario Kart to be used as a Gym environment.
We performed some initial reward shaping inspired on the Sethbling MarIQ code, that is explainned in a further section. We also publish here some of our experiments that include several environment wrappers, preprocessing and agent code that allow a faster initial experimentation with the game.
Youtube playlist with 4 agents trained on 4 different tracks - https://www.youtube.com/playlist?list=PL7CojJo9fzQ1Qm7-n20kDOlE1u-A3DvzA
pip install gym-retroYou need to copy the folder SuperMarioKart-Snes to your retro site-packages stable folder where the retro package is installed. Ending up with a path like site-packages/retro/data/stable/SuperMarioKart-Snes.
If you are using Anaconda your retro data stable path should be something like:
#Linux
/home/user/miniconda3/envs/env_name/lib/python3.7/site-packages/retro/data/stable
#Windows
C:/Users/userName/Miniconda3/envs/env_name/Lib/site-packages/retro/data/stableCould be useful to make an symbolic link between the SuperMarioKart-Snes installed on the retro library and git folder to allow easier experimentation and modification
After this you will be able to import the game by doing
import retro
env = retro.make('SuperMarioKart-Snes')
env.reset()
for i in range(1000):
obs,_,_,_ = env.step([1,0,0,0,0,0,0,0,0,0,0,0])
env.render()You can selected a certain state by doing (check the states in the folder)
env = retro.make('SuperMarioKart-Snes','RainbowRoad_M')You can also try to play the game by doing
python -m retro.examples.interactive --game SuperMarioKart-SnesPython requirements:
pip install --upgrade tensorflow==1.13.2
pip install stable-baselines[mpi]If you are using a windows machine, you might need to install the Windows Message Passing Interface (MPI): https://www.microsoft.com/en-us/download/details.aspx?id=57467
(OPTIONAL for configuring environment in retro and done/reward shaping )
Windows: https://storage.googleapis.com/gym-retro/builds/Gym%20Retro-latest-win64.zip
Mac: https://storage.googleapis.com/gym-retro/builds/Gym%20Retro-latest-Darwin.dmg
Extract the files and put them in the retro package main folder: C:/Users/username/Miniconda3/envs/env_name/Lib/site-packages/retro
Then create a shortcut and run the .exe from there.
You can follow the guide : https://retro.readthedocs.io/en/latest/integration.html
You should ajust the settings in the main.py as you like and then just run it
python main.pyTo test the model you should make sure that the config is the same as the trained model, and then just load the file
python test_model_multiple.py --load best_model.zipAfter each run, a bk2 file should be created in the root directory. To transform a run in a video file you can run
python -m retro.scripts.playback_movie movies/<RunFile>.bk2To beat :)
| Track | Agent Best Time (min) |
|---|---|
| MarioCircuit1 | 1:08:03 |
| KoopaBeach1 | 1:13:49 |
| GhostValley1 | 1:13:15 |
| RainbowRoad | 1:50:27 |
If you get better results let us know!
Any contributions are greatly appreciated
The SuperMarioKart-Snes Game has the save stats for all tracks with the Mario on the Time Trial Setting. It could be interesting to also have save stats of different players and on the Mario GP setting.
It could also be interesting to integrate Sethbling's idea of creating a reduced observation based on the track surface around the player. Allowing maybe a better agent generalization between tracks
If you know a better way to do experimentation and change multiple parameters in a single place and also keep track of the parameters used for each experiment, please let me know!
- esteveste - Bernardo Esteves
- CubeSkyy - Miguel Coelho
- Aegiel - Francisco Lopes
Project for 2019-2020 Autonomous Agents and Multi-Agent Systems course in Instituto Superior Técnico