import cv2
import numpy as np
import math

# make parent directory available
import os,sys,inspect
sys.path.insert(1, os.path.join(sys.path[0], '..'))

from dqn import *
from battleship import *

if __name__ == "__main__":
    print ("HEYA")
    state_xform, action_xform = StateXform(), ActionXform()
    dqn_policy = DQN(state_xform, action_xform).to(device)
    dqn_target = DQN(state_xform, action_xform).to(device)

    params = {
            "BATCH_SIZE" : 128,
            "GAMMA" : 0.5 ,
            "EPS_START" : 0.5,
            "EPS_END" : 0.05,
            "EPS_DECAY" : 10000,
            "TARGET_UPDATE" : 100 ,
            "UPDATE_PER_ROLLOUT" : 5,
            "LEARNING_RATE" : 0.001,
            "REPLAY_SIZE" : 1000000 ,
            "num_initial_episodes" : 100,
            "num_episodes" : 100000,
            "game_bound" : L*L*0.75,
            }

    trainer = Trainer(params)
    trainer.train(dqn_policy, dqn_target, GameEnv)

7e1aca7d95afee767f8c99ad84b6b0263e97bb85
<mask token>

<mask token>
sys.path.insert(1, os.path.join(sys.path[0], '..'))
<mask token>
if __name__ == '__main__':
    print('HEYA')
    state_xform, action_xform = StateXform(), ActionXform()
    dqn_policy = DQN(state_xform, action_xform).to(device)
    dqn_target = DQN(state_xform, action_xform).to(device)
    params = {'BATCH_SIZE': 128, 'GAMMA': 0.5, 'EPS_START': 0.5, 'EPS_END':
        0.05, 'EPS_DECAY': 10000, 'TARGET_UPDATE': 100,
        'UPDATE_PER_ROLLOUT': 5, 'LEARNING_RATE': 0.001, 'REPLAY_SIZE': 
        1000000, 'num_initial_episodes': 100, 'num_episodes': 100000,
        'game_bound': L * L * 0.75}
    trainer = Trainer(params)
    trainer.train(dqn_policy, dqn_target, GameEnv)

import cv2
import numpy as np
import math
import os, sys, inspect
sys.path.insert(1, os.path.join(sys.path[0], '..'))
from dqn import *
from battleship import *
if __name__ == '__main__':
    print('HEYA')
    state_xform, action_xform = StateXform(), ActionXform()
    dqn_policy = DQN(state_xform, action_xform).to(device)
    dqn_target = DQN(state_xform, action_xform).to(device)
    params = {'BATCH_SIZE': 128, 'GAMMA': 0.5, 'EPS_START': 0.5, 'EPS_END':
        0.05, 'EPS_DECAY': 10000, 'TARGET_UPDATE': 100,
        'UPDATE_PER_ROLLOUT': 5, 'LEARNING_RATE': 0.001, 'REPLAY_SIZE': 
        1000000, 'num_initial_episodes': 100, 'num_episodes': 100000,
        'game_bound': L * L * 0.75}
    trainer = Trainer(params)
    trainer.train(dqn_policy, dqn_target, GameEnv)

import cv2
import numpy as np
import math

# make parent directory available
import os,sys,inspect
sys.path.insert(1, os.path.join(sys.path[0], '..'))

from dqn import *
from battleship import *

if __name__ == "__main__":
    print ("HEYA")
    state_xform, action_xform = StateXform(), ActionXform()
    dqn_policy = DQN(state_xform, action_xform).to(device)
    dqn_target = DQN(state_xform, action_xform).to(device)

    params = {
            "BATCH_SIZE" : 128,
            "GAMMA" : 0.5 ,
            "EPS_START" : 0.5,
            "EPS_END" : 0.05,
            "EPS_DECAY" : 10000,
            "TARGET_UPDATE" : 100 ,
            "UPDATE_PER_ROLLOUT" : 5,
            "LEARNING_RATE" : 0.001,
            "REPLAY_SIZE" : 1000000 ,
            "num_initial_episodes" : 100,
            "num_episodes" : 100000,
            "game_bound" : L*L*0.75,
            }

    trainer = Trainer(params)
    trainer.train(dqn_policy, dqn_target, GameEnv)
