_, _, screen_height, screen_width = init_screen.shape
# Get number of actions from gym action space
n_actions = 4
policy_net = DQN(screen_height, screen_width, n_actions,layers=20).to(device)
target_net = DQN(screen_height, screen_width, n_actions,layers=20).to(device)
PATH = 'C:/Users/sagau/Google Drive/smaller1.pth'
optimizer = optim.Adam(policy_net.parameters(),lr=1e-4)
load_mode = False
if load_mode:
model_dict = torch.load(PATH,map_location=torch.device('cpu'))
i_episode = model_dict['epoch']
optimizer.load_state_dict(model_dict['optimizer'])
policy_net.load_state_dict(model_dict['state_dict'])
target_net.load_state_dict(model_dict['state_dict'])
episode_durations = model_dict['episode_durations']
total_reward_list = model_dict['total_reward_list']
point_list = model_dict['point_list']
plot_durations()
else:
i_episode = 0
target_net.load_state_dict(policy_net.state_dict())
target_net.eval()
episode_durations = []
total_reward_list = []
point_list = []
memory = ReplayMemory(10000000)
steps_done = 0
######################################################################
init_screen = get_screen()
_, _, screen_height, screen_width = init_screen.shape
# Get number of actions from gym action space
n_actions = 4
#PATH = 'C:/Users/sagau/Desktop/Kaggle/TetrisRepo/models/model1_2.pth'
policy_net = DQN(screen_height, screen_width, n_actions,layers=20)
policy_net.eval()
policy_net = DQN(screen_height, screen_width, n_actions,layers=20).to(device)
PATH = 'C:/Users/sagau/Google Drive/transfersmaller.pth'
model_dict = torch.load(PATH,map_location=torch.device('cpu'))
policy_net.load_state_dict(model_dict['state_dict'])
######################################################################
# Play with model !
sleep_time = 0.2
game = Tetris(nb_rows=8,nb_cols=6)
done = game.generate_block(choice=random.randint(0,3))
rows = 0
for t in count():
# for t in range(200):
state = get_screen()
action = select_action(state,0)
# print(action.item())
game.play_active_block(action.item())
if game.block_reached_end():
rows = rows + game.clear_rows()