https://github.com/wirelessr/heartsenv
This repo is made for the gym enivornment with Hearts
https://github.com/wirelessr/heartsenv
gym gym-environment hearts
Last synced: 5 months ago
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This repo is made for the gym enivornment with Hearts
- Host: GitHub
- URL: https://github.com/wirelessr/heartsenv
- Owner: wirelessr
- License: mit
- Created: 2018-08-10T08:34:01.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2022-12-08T02:22:01.000Z (almost 3 years ago)
- Last Synced: 2024-03-30T11:10:15.052Z (over 1 year ago)
- Topics: gym, gym-environment, hearts
- Language: Python
- Homepage:
- Size: 104 KB
- Stars: 5
- Watchers: 3
- Forks: 2
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://travis-ci.org/wirelessr/HeartsEnv)
[](https://codecov.io/gh/wirelessr/HeartsEnv)
### Description
- 1.0.0 Full rules of Hearts has been supported
#### Single Player Enviroment
The environment is based on standard gym, so you can use `reset`, `step`, `render`, etc. to control the game.
Moreover, the action space supports `sample` and `contains`. When you cannot consider what action you should take,
you can use `sample` to generate a regular action.
The `render` supports two modes, *human* and *ansi*; the human mode can display colored cards, and the ansi mode
display only the text.
Every action you feed into `step` **MUST** pass the validation, or the environment throws the exception instead.
You can check the type of exception to clarify what rule you violated.
```python
from hearts.single import SingleEnv
done = False
env = SingleEnv()
env.reset()
while not done:
env.render()
# You can simply use sample() to finish the whole game
action = env.action_space.sample()
obs, reward, done, _ = self.env.step(action)
# Do whatever learning algorithm
```
#### Special rules
This environment has also been applied the special rules from **TrendMicro 2018 AI Contests** in the *trend_rule* branch.
#### Observation
The observation is a tuple consist of two parts; one is player information, which are score, hand cards and receiving cards, not to mention that only you yourself can see the hand cards. Another is table information including many states, such as round, start position, current position, pass or not, was there a heart broken, how many games, expose hearts or not, is there any exposed, borad cards, the leading card at current round, and the last board cards.
The most important thing is the player is always at the **position 3**, so that you can check the index of board or backup to get the other opponents' information.
The detail is at [single.py](https://github.com/wirelessr/HeartsEnv/blob/trend_rule/hearts/single.py)
```python
self.observation_space = spaces.Tuple([
# player states
spaces.Tuple([
spaces.Tuple([ # p0, p1, p2
spaces.Discrete(200), # score
spaces.Tuple([ # income
spaces.MultiDiscrete([13, 4])
] * 52),
] * 3),
spaces.Discrete(200), # p3 score
spaces.Tuple([ # hand
spaces.MultiDiscrete([13, 4])
] * 13),
spaces.Tuple([ # income
spaces.MultiDiscrete([13, 4])
] * 52),
]),
# table states
spaces.Tuple([
spaces.Discrete(13), # n_round
spaces.Discrete(4), # start_pos
spaces.Discrete(4), # cur_pos
spaces.Discrete(1), # exchanged
spaces.Discrete(1), # heart_occured
spaces.Discrete(100), # n_games
spaces.Discrete(1), # finish_expose
spaces.Discrete(1), # heart_exposed
spaces.Tuple([ # board
spaces.MultiDiscrete([13, 4])
] * 4),
spaces.Tuple([ # first_draw
spaces.MultiDiscrete([13, 4])
]),
spaces.Tuple([ # backup
spaces.MultiDiscrete([13, 4])
] * 4)
]),
])
```
### Reference
[gym.env](https://github.com/openai/gym/blob/master/gym/core.py)
[Slides](https://docs.google.com/presentation/d/1MdtczHpVs6iht5Z_NQh_97ZHCyOkWD1f01pTfKBuW9s/edit?usp=sharing)
## How to use
1. Install docker and docker-compose
1. [install docker](https://docs.docker.com/install/)
1. [install docker compose](https://docs.docker.com/compose/install/#install-compose)
1. Goto docker-compose working directory
```
cd HeartsEnv
1. Build docker image
```
make
1. Start docker containers
```
docker-compose up -d
1. Run poker bot
```
docker-compose exec hearts python /hearts/demo/demo_hearts.py
## Jupyter (iPython)
1. Open browser
```
http://localhost:8888/
## View OpenGL output
1. Open browser
```
http://localhost:6080/
## Other commands
1. In docker-compose working directory
1. Remove all docker containers
```
docker-compose down
1. Get shell of docker containers
```
docker-compose exec hearts bash