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https://github.com/ishikota/PyPokerEngine
Poker engine for poker AI development in Python
https://github.com/ishikota/PyPokerEngine
ai-poker-game poker-engine reinforcement-learning
Last synced: 3 months ago
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Poker engine for poker AI development in Python
- Host: GitHub
- URL: https://github.com/ishikota/PyPokerEngine
- Owner: ishikota
- License: mit
- Created: 2016-06-05T03:31:05.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2024-04-10T13:40:46.000Z (7 months ago)
- Last Synced: 2024-07-19T11:18:05.294Z (4 months ago)
- Topics: ai-poker-game, poker-engine, reinforcement-learning
- Language: Python
- Homepage: https://ishikota.github.io/PyPokerEngine/
- Size: 661 KB
- Stars: 593
- Watchers: 36
- Forks: 182
- Open Issues: 16
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
- awesome-game-ai - [code
README
# PyPokerEngine
[](https://travis-ci.org/ishikota/PyPokerEngine)
[](https://coveralls.io/github/ishikota/PyPokerEngine?branch=master)
[](https://badge.fury.io/py/PyPokerEngine)
[](https://github.com/ishikota/kyoka/blob/master/LICENSE.md)
Poker engine for AI development in Python
# Tutorial
This tutorial leads you to start point of poker AI development!!
#### Outline of Tutorial
1. Create simple AI which always returns same action.
2. Play AI vs AI poker game and see its result.
#### Installation
Before start AI development, we need to install *PyPokerEngine*.
You can use pip like this.
```
pip install PyPokerEngine
```
This library supports Python 2 (2.7) and Python3 (3.5).
## Create first AI
In this section, we create simple AI which always declares *CALL* action.
To create poker AI, what we do is following
1. Create PokerPlayer class which is subclass of [`PypokerEngine.players.BasePokerPlayer`](https://github.com/ishikota/PyPokerEngine/blob/master/pypokerengine/players/base_poker_player.py).
2. Implement abstract methods which inherit from `BasePokerPlayer` class.
Here is the code of our first AI. (We assume you saved this file at `~/dev/fish_player.py`)
```python
from pypokerengine.players import BasePokerPlayer
class FishPlayer(BasePokerPlayer): # Do not forget to make parent class as "BasePokerPlayer"
# we define the logic to make an action through this method. (so this method would be the core of your AI)
def declare_action(self, valid_actions, hole_card, round_state):
# valid_actions format => [raise_action_info, call_action_info, fold_action_info]
call_action_info = valid_actions[1]
action, amount = call_action_info["action"], call_action_info["amount"]
return action, amount # action returned here is sent to the poker engine
def receive_game_start_message(self, game_info):
pass
def receive_round_start_message(self, round_count, hole_card, seats):
pass
def receive_street_start_message(self, street, round_state):
pass
def receive_game_update_message(self, action, round_state):
pass
def receive_round_result_message(self, winners, hand_info, round_state):
pass
```
If you are interested in what each callback method receives, See [AI_CALLBACK_FORMAT.md](https://github.com/ishikota/PyPokerEngine/blob/master/AI_CALLBACK_FORMAT.md).
## Play AI vs AI poker game
Ok, let's play the poker game by using our created `FishPlayer`.
To start the game, what we need to do is following
1. Define game rule through `Config` object (ex. start stack, blind amount, ante, blind_structures)
2. Register your AI with `Config` object.
3. Start the game and get game result
Here is the code to play poker for 10 round with our created `FishPlayer`.
```python
from pypokerengine.api.game import setup_config, start_poker
config = setup_config(max_round=10, initial_stack=100, small_blind_amount=5)
config.register_player(name="p1", algorithm=FishPlayer())
config.register_player(name="p2", algorithm=FishPlayer())
config.register_player(name="p3", algorithm=FishPlayer())
game_result = start_poker(config, verbose=1)
```
We set `verbose=1`, so simple game logs are output after `start_poker` call.
```
Started the round 1
Street "preflop" started. (community card = [])
"p1" declared "call:10"
"p2" declared "call:10"
"p3" declared "call:10"
Street "flop" started. (community card = ['C4', 'C6', 'CA'])
"p2" declared "call:0"
"p3" declared "call:0"
"p1" declared "call:0"
Street "turn" started. (community card = ['C4', 'C6', 'CA', 'D4'])
"p2" declared "call:0"
"p3" declared "call:0"
"p1" declared "call:0"
Street "river" started. (community card = ['C4', 'C6', 'CA', 'D4', 'H2'])
"p2" declared "call:0"
"p3" declared "call:0"
"p1" declared "call:0"
"['p3']" won the round 1 (stack = {'p2': 90, 'p3': 120, 'p1': 90})
Started the round 2
...
"['p1']" won the round 10 (stack = {'p2': 30, 'p3': 120, 'p1': 150})
```
Finally, let's check the game result !!
```python
>>> print game_result
{
'rule': {'ante': 0, 'blind_structure': {}, 'max_round': 10, 'initial_stack': 100, 'small_blind_amount': 5},
'players': [
{'stack': 150, 'state': 'participating', 'name': 'p1', 'uuid': 'ijaukuognlkplasfspehcp'},
{'stack': 30, 'state': 'participating', 'name': 'p2', 'uuid': 'uadjzyetdwsaxzflrdsysj'},
{'stack': 120, 'state': 'participating', 'name': 'p3', 'uuid': 'tmnkoazoqitkzcreihrhao'}
]
}
```
## GUI support
We also provide GUI application. You can play poker with your AI on browser.
Please check [PyPokerGUI](https://github.com/ishikota/PyPokerGUI).
# for Reinforcement Learning users
`PyPokerEngine` is developed for Reinforcement Learning usecase.
So we also provide `Emulator` class which has convinient methods for Reinforcement Learning.
Common usage of `Emulator` would be like below.
```python
from pypokerengine.players import BasePokerPlayer
from pypokerengine.api.emulator import Emulator
from pypokerengine.utils.game_state_utils import restore_game_state
from mymodule.poker_ai.player_model import SomePlayerModel
class RLPLayer(BasePokerPlayer):
# Setup Emulator object by registering game information
def receive_game_start_message(self, game_info):
player_num = game_info["player_num"]
max_round = game_info["rule"]["max_round"]
small_blind_amount = game_info["rule"]["small_blind_amount"]
ante_amount = game_info["rule"]["ante"]
blind_structure = game_info["rule"]["blind_structure"]
self.emulator = Emulator()
self.emulator.set_game_rule(player_num, max_round, small_blind_amount, ante_amount)
self.emulator.set_blind_structure(blind_structure)
# Register algorithm of each player which used in the simulation.
for player_info in game_info["seats"]["players"]:
self.emulator.register_player(player_info["uuid"], SomePlayerModel())
def declare_action(self, valid_actions, hole_card, round_state):
game_state = restore_game_state(round_state)
# decide action by using some simulation result
# updated_state, events = self.emulator.apply_action(game_state, "fold")
# updated_state, events = self.emulator.run_until_round_finish(game_state)
# updated_state, events = self.emulator.run_until_game_finish(game_state)
if self.is_good_simulation_result(updated_state):
return # you would declare CALL or RAISE action
else:
return "fold", 0
```
# Documentation
For mode detail, please checkout [doc site](https://ishikota.github.io/PyPokerEngine/)