def __init__(self, rand_seed, display=False, no_op_max=7):
self.env = gym.make(visual=VISUAL, game=GAME)
self.state_size = self.env.observation_space_shape
self.action_size = len(self.env.action_space)
self._no_op_max = no_op_max
if display:
self._setup_display()
self.reset()
import numpy as np
from game import mygym as gym
from training.ddqn import DQNAgent
import constants
import cv2
EPISODES = 5000
if __name__ == "__main__":
visual = constants.VISUAL
verbose = False
env = gym.make(visual=visual, game='CDGame')
state_size = env.observation_space_shape
action_size = len(env.action_space)
agent = DQNAgent(state_size, action_size)
# agent.load("training/save/cd-ddqn.h5")
done = False
batch_size = 32
max_step = 500
for e in range(EPISODES):
print('Episode {}/{}'.format(e + 1, EPISODES))
state = env.reset()
state = np.reshape(state,
[1, state_size[0], state_size[1], state_size[2]])
for time in range(max_step):
action = agent.act(state)
next_state, reward, done, total_reward = env.step(action)
if visual:
import numpy as np
from game import mygym as gym
from training.ddqn_gw import DQNAgent
import cv2
EPISODES = 5000
if __name__ == "__main__":
visual = True
verbose = False
env = gym.make(visual=visual, game='GridWorld')
state_size = env.observation_space_shape
action_size = len(env.action_space)
agent = DQNAgent(state_size, action_size)
# agent.load("training/save/gw-ddqn.h5")
done = False
batch_size = 32
max_step = 500
for e in range(EPISODES):
print('Episode {}/{}'.format(e + 1, EPISODES))
state = env.reset()
state = np.reshape(state, [1, state_size])
for time in range(max_step):
action = agent.act(state)
next_state, reward, done, total_reward = env.step(action, agent)
next_state = np.reshape(next_state, [1, state_size])
if visual:
cv2.imshow('state', next_state)
cv2.waitKey(10)
import game.mygym as gym
import pygame
import cv2
if __name__ == '__main__':
env = gym.make()
state = env.reset()
carryOn = True
while carryOn:
next_state, reward, done, _ = env.step(2)
pygame.quit()