import argparse
"""
Makes a DeepQ Agent and runs it through one fight for each character in the roster so the user can view it
"""
if __name__ == "__main__":
parser = argparse.ArgumentParser(description= 'Imports the specified class library and loads the specificed model.')
parser.add_argument('-cn', '--className', type= str, default= "DeepQAgent", help= 'Name of the class library to import')
parser.add_argument('-mn', '--modelName', type= str, default= None, help= 'Name of the specific model to be loaded and tested')
parser.add_argument('-l', '--load', action= 'store_true', help= 'Boolean flag for if the user wants to load pre-existing weights')
parser.add_argument('-c', '--character', type= str, default= "ryu", help= 'The specific character this agent will play')
args = parser.parse_args()
if args.className is None:
args.modelName = args.className
agent = None
exec("from {0} import {0}".format(args.className))
exec("agent = {0}(load= {1}, name= \"{2}\", character= \"{3}\")".format(args.className, args.load, args.modelName, args.character))
from Lobby import Lobby
testLobby = Lobby()
testLobby.addPlayer(agent)
testLobby.executeTrainingRun(review= False, render= True)
The training data for the model
model
The model for the function to train
Returns
-------
model
The now trained and hopefully improved model
"""
raise NotImplementedError("Implement this is in the inherited agent")
### End of Abstract methods
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Processes agent parameters.')
parser.add_argument(
'-r',
'--render',
action='store_true',
help=
'Boolean flag for if the user wants the game environment to render during play'
)
args = parser.parse_args()
from Lobby import Lobby
testLobby = Lobby(render=args.render)
agent = Agent()
testLobby.addPlayer(agent)
testLobby.executeTrainingRun()
'Boolean flag for if the user wants the game environment to render during play'
)
parser.add_argument(
'-l',
'--load',
action='store_true',
help='Boolean flag for if the user wants to load pre-existing weights')
parser.add_argument(
'-e',
'--episodes',
type=int,
default=10,
help=
'Intger representing the number of training rounds to go through, checkpoints are made at the end of each episode'
)
parser.add_argument(
'-n',
'--name',
type=str,
default=None,
help=
'Name of the instance that will be used when saving the model or it\'s training logs'
)
args = parser.parse_args()
qAgent = DeepQAgent(load=args.load, name=args.name)
from Lobby import Lobby
testLobby = Lobby()
testLobby.addPlayer(qAgent)
testLobby.executeTrainingRun(episodes=args.episodes, render=args.render)
from DeepQAgent import *
"""Makes a DeepQ Agent and runs it through one fight for each character in the roster so the user can view it"""
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Processes agent parameters.')
parser.add_argument(
'-n',
'--name',
type=str,
default=None,
help=
'Name of the instance that will be used when saving the model or it\'s training logs'
)
args = parser.parse_args()
qAgent = DeepQAgent(load=True, epsilon=0, name=args.name)
from Lobby import Lobby
testLobby = Lobby(render=True)
testLobby.addPlayer(qAgent)
testLobby.executeTrainingRun(review=False)
'Boolean flag for if the user wants the game environment to render during play'
)
parser.add_argument(
'-l',
'--load',
action='store_true',
help='Boolean flag for if the user wants to load pre-existing weights')
parser.add_argument(
'-e',
'--episodes',
type=int,
default=10,
help=
'Intger representing the number of training rounds to go through, checkpoints are made at the end of each episode'
)
parser.add_argument(
'-n',
'--name',
type=str,
default=None,
help=
'Name of the instance that will be used when saving the model or it\'s training logs'
)
args = parser.parse_args()
qAgent = DeepQAgent(load=args.load, name=args.name)
from Lobby import Lobby
testLobby = Lobby(render=args.render)
testLobby.addPlayer(qAgent)
testLobby.executeTrainingRun(episodes=args.episodes)
