def load_NetPlayer(board, args):
num_nodes = board.world.map_graph.number_of_nodes()
num_edges = board.world.map_graph.number_of_edges()
model_args = misc.read_json(args["model_parameters_json"])
net = GCN_risk(
num_nodes, num_edges, model_args['board_input_dim'],
model_args['global_input_dim'], model_args['hidden_global_dim'],
model_args['num_global_layers'], model_args['hidden_conv_dim'],
model_args['num_conv_layers'], model_args['hidden_pick_dim'],
model_args['num_pick_layers'], model_args['out_pick_dim'],
model_args['hidden_place_dim'], model_args['num_place_layers'],
model_args['out_place_dim'], model_args['hidden_attack_dim'],
model_args['num_attack_layers'], model_args['out_attack_dim'],
model_args['hidden_fortify_dim'], model_args['num_fortify_layers'],
model_args['out_fortify_dim'], model_args['hidden_value_dim'],
model_args['num_value_layers'], model_args['dropout'])
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
net.to(device)
net.eval()
state_dict = load_dict(args["model_path"], device='cpu', encoding='latin1')
net.load_state_dict(state_dict['model'])
apprentice = agent.NetApprentice(net)
kwargs = {}
for a in ["move_selection", "name", "temp"]:
if a in args: kwargs[a] = args[a]
netPlayer = agent.NetPlayer(apprentice, **kwargs)
return netPlayer
# Get data now
models_sorted = get_model_order(path_model)
wins = []
netPlayer_countries = []
netPlayer_armies = []
op_countries = []
op_armies = []
model_cont = []
for i, model_name in enumerate(models_sorted):
a = re.search(f"[a-z]+_[0-9]+_{match_number}", model_name)
a = 1
if a is None: continue
print(f"Chosen model is {model_name}")
state_dict = load_dict(os.path.join(path_model, model_name),
device='cpu',
encoding='latin1')
net.load_state_dict(state_dict['model'])
net.eval()
for k in range(num_matchs):
if (k + 1) % 10 == 0: print(f'Match {k+1}')
world = World(path_board)
apprentice = agent.NetApprentice(net)
netPlayer = agent.NetPlayer(apprentice,
move_selection="random_proportional",
temp=0.5)
# Play against random
pRandom = RandomAgent('Random')
battle_board = Board(world, [netPlayer, pRandom])
battle_board.setPreferences(prefs)
for j in range(max_turns):
def create_self_play_script(input_file, move_type, verbose):
# ---------------- Start -------------------------
inputs = misc.read_json(input_file)
misc.print_and_flush("create_self_play: Start")
start = time.process_time()
saved_states_per_episode = inputs["saved_states_per_episode"]
max_episode_depth = inputs["max_episode_depth"]
apprentice_params = inputs["apprentice_params"]
expert_params = inputs["expert_params"]
path_data = inputs["path_data"]
path_model = inputs["path_model"]
model_args = misc.read_json(inputs["model_parameters"])
board_params = inputs["board_params"]
path_board = board_params["path_board"]
move_types = ["initialPick", "initialFortify", "startTurn", "attack", "fortify"]
# ---------------------------------------------------------------
# Create board
world = World(path_board)
# Set players
pR1, pR2 = agent.RandomAgent('Red'), agent.RandomAgent('Blue')
players = [pR1, pR2]
# Set board
prefs = board_params
board_orig = Board(world, players)
board_orig.setPreferences(prefs)
num_nodes = board_orig.world.map_graph.number_of_nodes()
num_edges = board_orig.world.map_graph.number_of_edges()
if apprentice_params["type"] == "net":
if verbose: misc.print_and_flush("create_self_play: Creating model")
net = GCN_risk(num_nodes, num_edges,
model_args['board_input_dim'], model_args['global_input_dim'],
model_args['hidden_global_dim'], model_args['num_global_layers'],
model_args['hidden_conv_dim'], model_args['num_conv_layers'],
model_args['hidden_pick_dim'], model_args['num_pick_layers'], model_args['out_pick_dim'],
model_args['hidden_place_dim'], model_args['num_place_layers'], model_args['out_place_dim'],
model_args['hidden_attack_dim'], model_args['num_attack_layers'], model_args['out_attack_dim'],
model_args['hidden_fortify_dim'], model_args['num_fortify_layers'], model_args['out_fortify_dim'],
model_args['hidden_value_dim'], model_args['num_value_layers'],
model_args['dropout'])
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
net.to(device)
model_name = apprentice_params["model_name"]
if model_name: # If it is not the empty string
try:
if verbose: misc.print_and_flush(f"create_self_play : Chosen model is {model_name}")
state_dict = load_dict(os.path.join(path_model, model_name), device = 'cpu', encoding = 'latin1')
net.load_state_dict(state_dict['model'])
if verbose: misc.print_and_flush("create_self_play: Model has been loaded")
except Exception as e:
print(e)
if verbose: misc.print_and_flush("create_self_play: Defining net apprentice")
# Define initial apprentice
apprentice = agent.NetApprentice(net)
else:
if verbose: misc.print_and_flush("create_self_play: Defining MCTS apprentice")
apprentice = agent.MctsApprentice(num_MCTS_sims = apprentice_params["num_MCTS_sims"],
temp = apprentice_params["temp"],
max_depth = apprentice_params["max_depth"],
sims_per_eval = apprentice_params["sims_per_eval"])
if verbose: misc.print_and_flush("create_self_play: Defining expert")
# build expert
expert = build_expert_mcts(apprentice, max_depth=expert_params["max_depth"],
sims_per_eval=expert_params["sims_per_eval"], num_MCTS_sims=expert_params["num_MCTS_sims"],
wa = expert_params["wa"], wb = expert_params["wb"],
cb = expert_params["cb"], use_val = expert_params["use_val"])
if verbose: misc.print_and_flush("create_self_play: Creating data folders")
# Create folders to store data
for folder in move_types:
os.makedirs(os.path.join(path_data, folder, 'raw'), exist_ok = True)
os.makedirs(path_model, exist_ok = True)
#### START
start_inner = time.process_time()
state = copy.deepcopy(board_orig)
# Play episode, select states to save
if verbose: misc.print_and_flush("create_self_play: Self-play")
states_to_save = create_self_play_data(move_type, path_data, state, apprentice, max_depth = max_episode_depth, saved_states_per_episode=saved_states_per_episode, verbose = verbose)
if verbose: misc.print_and_flush(f"create_self_play: Play episode: Time taken: {round(time.process_time() - start_inner,2)}")
# Tag the states and save them
start_inner = time.process_time()
if verbose: misc.print_and_flush(f"create_self_play: Tag the states ({len(states_to_save)} states to tag)")
for st in states_to_save:
st_tagged, policy_exp, value_exp = tag_with_expert_move(st, expert, temp=expert_params["temp"], verbose=verbose)
_ = simple_save_state(path_data, st_tagged, policy_exp, value_exp, verbose=verbose)
if verbose: misc.print_and_flush(f"create_self_play: Tag and save: Time taken -> {round(time.process_time() - start_inner,2)}")
misc.print_and_flush(f"create_self_play: Total time taken -> {round(time.process_time() - start,2)}")