ENV

ENV - Environment for Reinforcement Learning

ENV 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 ENV library, use:

• Using pip:

    Update later
  

• Directly from source (recommended):

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

API

from setup import make
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('SushiGo')
env.numba_main_2(Agent, 1000, [0], 0)
# count_win, agent_data = env.numba_main_2(Agent, count_game_train, agent_data, level)

Example

Please refer to Wiki for complete usage details

Environment

ENV includes 20 games:

Game	Win lv0	win lv1	win lv1	Time lv0	Time lv1	Time lv1
Catan	2535	211	13	307	224	324
CatanNoExchange	2464	339	False	190	673	False
Century	1932	12	1	48	51	52
Durak	2561	447	False	15	18	False
Exploding_Kitten	2002	1631	False	18	16	False
Fantan	2413	117	False	27	63	False
GoFish	2536	2499	False	10	12	False
Imploding_Kitten	1659	1316	False	37	39	False
MachiKoro	2542	53	7	13	14	18
Phom	2444	500	False	31	33	False
Poker	1109	1155	False	71	59	False
Sheriff	2510	3	362	28	30	33
Splendor	2449	28	1	114	154	90
Splendor_v2	2621	15	1	48	51	50
Splendor_v3	2615	677	29	29	26	36
StoneAge	2467	36	0	92	217	133
SushiGo	2010	141	154	11	14	14
TicketToRide	2041	0	False	60	913	False
TLMN	2456	772	262	16	17	26
WelcomeToTheDungeon_v1	2530	833	796	3	12	15
WelcomeToTheDungeon_v2	2481	914	464	4	15	14

Please refer to Wiki for more details.

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