"""manual keypress agent"""
from keras import Sequential
from keras.layers import Dense, Dropout
from rl.memory import SequentialMemory
from agents.agent_keras_rl_dqn import TrumpPolicy, memory_limit, window_length
from gym_env import env
class Player:
"""Mandatory class with the player methods"""
def __init__(self, name='Custom_Q1'):
"""Initiaization of an agent"""
self.equity_alive = 0
self.actions = []
self.last_action_in_stage = ''
self.temp_stack = []
self.name = name
self.autoplay = True
self.model = None
def initiate_agent(self, nb_actions):
"""initiate a deep Q agent"""
self.model = Sequential()
self.model.add(Dense(512, activation='relu', input_shape=env.observation_space)) # pylint: disable=no-member
self.model.add(Dropout(0.2))
self.model.add(Dense(512, activation='relu'))
self.model.add(Dropout(0.2))
self.model.add(Dense(512, activation='relu'))
self.model.add(Dropout(0.2))
self.model.add(Dense(nb_actions, activation='linear'))
# Finally, we configure and compile our agent. You can use every built-in Keras optimizer and
# even the metrics!
memory = SequentialMemory(limit=memory_limit, window_length=window_length) # pylint: disable=unused-variable
policy = TrumpPolicy() # pylint: disable=unused-variable
def action(self, action_space, observation, info): # pylint: disable=no-self-use,unused-argument
"""Mandatory method that calculates the move based on the observation array and the action space."""
_ = (observation, info) # not using the observation for random decision
action = None
# decide if explore or explot
# forward
# save to memory
# backward
# decide what to use for training
# update model
# save weights
return action
