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https://github.com/sofianedjerbi/pyboyenv
Turn any gameboy game into a reinforcement learning environment.
https://github.com/sofianedjerbi/pyboyenv
environment reinforcement-learning reinforcement-learning-environments
Last synced: 2 months ago
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Turn any gameboy game into a reinforcement learning environment.
- Host: GitHub
- URL: https://github.com/sofianedjerbi/pyboyenv
- Owner: sofianedjerbi
- License: mit
- Created: 2020-04-26T01:27:35.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2023-04-18T21:42:28.000Z (over 1 year ago)
- Last Synced: 2024-04-05T16:30:04.910Z (9 months ago)
- Topics: environment, reinforcement-learning, reinforcement-learning-environments
- Language: Python
- Homepage:
- Size: 10.3 MB
- Stars: 7
- Watchers: 1
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# PyBoyEnv
This package allow you to turn any gameboy memory event into a reinforcement learning environment rule.
(generated with test.py)
## Installation
Install the dependencies:
```bash
pip install -r requirements.txt
```
Install the package with `pip`:
```bash
pip install ./
```
## Quickstart
```python
import gym
import pyboyenv
# Create the environment
env = gym.make('Pyboy-v0', game=)
# Add rules
env.set_reward_rule(ADDRESS, TYPE, VALUE, LABEL)
# Available rules:
# - increase: add VALUE if the memory at the address ADDRESS increases
# - decrease: add VALUE if the memory at the address ADDRESS decreases
# - smaller X: add VALUE if the memory at the address ADDRESS is smaller than X
# - bigger X: add VALUE if the memory at the address ADDRESS is bigger than X
# - equals X: add VALUE if the memory at the address ADDRESS equals X
# - in X1,..,XN: add VALUE if the memory at the address ADDRESS is equal to X1 or ... or XN
# Useful values
cumul = 0 # Sum of rewards
done = False # Is the game done ?
action = 16 # Initial action (action list available below, 16=nothing)
# Game loop
while not done:
# State = Window screen
state, reward, done, info = env.step(action)
cumul += reward
action = Agent.get_action(state, cumul, reward) # Next action
# Print infos
for i in info:
print(f"{i[0]}: {i[1]}")
```
## Actions
- 0: `WindowEvent.PRESS_ARROW_UP`
- 1: `WindowEvent.PRESS_ARROW_DOWN`
- 2: `WindowEvent.PRESS_ARROW_LEFT`
- 3: `WindowEvent.PRESS_ARROW_RIGHT`
- 4: `WindowEvent.PRESS_BUTTON_A`
- 5: `WindowEvent.PRESS_BUTTON_B`
- 6: `WindowEvent.PRESS_BUTTON_SELECT`
- 7: `WindowEvent.PRESS_BUTTON_START`
- 8: `WindowEvent.RELEASE_ARROW_UP`
- 9: `WindowEvent.RELEASE_ARROW_DOWN`
- 10: `WindowEvent.RELEASE_ARROW_LEFT`
- 11: `WindowEvent.RELEASE_ARROW_RIGHT`
- 12: `WindowEvent.RELEASE_BUTTON_A`
- 13: `WindowEvent.RELEASE_BUTTON_B`
- 14: `WindowEvent.RELEASE_BUTTON_SELECT`
- 15: `WindowEvent.RELEASE_BUTTON_START`
- 16: `WindowEvent.PASS`