ngoxuanphong / ENV

Reinforcement Learning System

VIS - Environment for Reinforcement Learning

vis is an open source Python library for developing and comparing reinforcement learning algorithms by providing a standard API to communicate between learning algorithms and environments, as well as a standard set of environments compliant with that API

Installation

We are support Python 3.7, 3.8, 3.9, 3.10 on Linux and Windows
To install the base vis library, use:

• Using pip:

  Update later

• Directly from source (recommended):

  git clone https://github.com/ngoxuanphong/vis.git
  cd vis
  pip install -r requirements.txt

API

import env

env.make()
# Run game
print(env.run())

# Visualization one game
env.render(Agent = env.Agent_random)

#Play with bot in game
env.render(Agent = 'human')

import env
from numba import njit
import numpy as np

@njit()
def Agent(state, agent_data):
    validActions = env.getValidActions(state)
    actions = np.where(validActions==1)[0]
    action = np.random.choice(actions)
    return action, agent_data
    
env.make()
env.run(Agent, num_game = 1000, agent_model = [0], level = 0)

env.render(Agent = Agent)

Example

Please refer to Wiki for complete usage details

Environment

vis includes 20 games, which are listed below along with the number of actions, observations, time run, win rate they have:

Game	Win-lv0	win-lv1	win-lv2	Time-lv0	Time-lv1	Time-lv2	Graphics	Link
Catan	2440	354	7	252	230	183	True	Catan
CatanNoExchange	2491	424	22	168	1021	96	True	CatanNoExchange
Century	1970	7		36	61		True	Century
Durak	2540	460		15	21		True	Durak
Exploding_Kitten	2044	1695		24	35		True	Exploding_Kitten
Fantan	2464	114		17	31			Fantan
GoFish	2466	2517	497	9	34	13	True	GoFish
Imploding_Kitten	1646	1274		62	51		True	Imploding_Kitten
MachiKoro	2468	31	3	11	9	8	True	MachiKoro
Poker	1100	1112		81	127		True	Poker
Sheriff	2481	4	365	29	29	19	True	Sheriff
Splendor	2517	21	4	105	154	85	True	Splendor
Splendor_v2	2632	9	4	50	180	123	True	Splendor_v2
Splendor_v3	2700	681	28	34	33	41	True	Splendor_v3
StoneAge	2485	40	0	65	229	100	True	StoneAge
SushiGo	2096	144	187	8	14	14	True	SushiGo
TicketToRide	2017	0		84	313		True	TicketToRide
TLMN	2532	766	279	23	25	31	True	TLMN
WelcomeToTheDungeon_v1	2512	830		5	11			WelcomeToTheDungeon_v1
WelcomeToTheDungeon_v2	2458	889	443	8	11	23		WelcomeToTheDungeon_v2

Please refer to Wiki for more details.