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agent.py
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agent.py
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import json
import pathlib
import sys
import time
import numpy as np
from scipy.special import softmax
from sortedcontainers import SortedSet
from src.engine import State, Action, ActionType
base_path = str(pathlib.Path(__file__).parent.absolute())
PATH_FIRST_MODEL = "models/1st.json"
PATH_SECOND_MODEL = "models/2nd.json"
start_time = None
def read_game_input():
# read players info
game_input = [input(), input()]
# read cards in hand and actions from opponent
opp_hand, opp_actions = [int(i) for i in input().split()]
game_input.append(f"{opp_hand} {opp_actions}")
# read all opponent actions
for i in range(opp_actions):
game_input.append(input()) # opp action #i
# read card count
card_count = int(input())
game_input.append(str(card_count))
# read cards
for i in range(card_count):
game_input.append(input()) # card #i
return game_input
def encode_state(game_input):
# initialize empty state
state = np.zeros((3, 16), dtype=np.float32)
# get how many opponent action lines to skip
opp_actions = int(game_input[2].split()[1])
# put choices from player hand into the state
for i, card in enumerate(game_input[4 + opp_actions:]):
card = card.split()
card_type = [1.0 if int(card[3]) == i else 0.0 for i in range(4)]
cost = int(card[4]) / 12
attack = int(card[5]) / 12
defense = max(-12, int(card[6])) / 12
keywords = list(map(int, map(card[7].__contains__, 'BCDGLW')))
player_hp = int(card[8]) / 12
enemy_hp = int(card[9]) / 12
card_draw = int(card[10]) / 2
state[i] = card_type + [cost, attack, defense, player_hp,
enemy_hp, card_draw] + keywords
return state.flatten()
def eval_creature(creature):
return 0.9903654503260438 * creature.attack \
+ 0.8807415488815061 * creature.defense \
+ 0.2653632695271666 * creature.has_ability('B') \
+ 0.5596083779334705 * creature.has_ability('D') \
+ 0.07114298711816602 * creature.has_ability('G') \
+ 0.14339516451120193 * creature.has_ability('L') \
+ 0.2955693950317263 * creature.has_ability('W')
def eval_state(state):
score = 0
pl = state.current_player
op = state.opposing_player
# check opponent's death
if op.health <= 0:
score += 100000
# check own death
if pl.health <= 0:
score -= 100000
# health
score += 0.015603063115694038 * pl.health
score -= 0.015603063115694038 * op.health
# hand
score += 0.05022772028105327 * len(pl.hand)
score -= 0.05022772028105327 * len(op.hand)
# card strength
for pl_lane, op_lane in zip(pl.lanes, op.lanes):
lane_score = sum(0.9721034503143529 * eval_creature(c) for c in pl_lane)
lane_score -= sum(0.9721034503143529 * eval_creature(c) for c in op_lane)
score += 0.5381958101147939 * lane_score
return score
def act_on_battle(state, eval_function=eval_state):
# initialize score dict
# maximize state score primarily and minimize amount of actions secondarily
scores = dict({(): (eval_function(state), 0)})
# initialize open and closed sets
unvisited = SortedSet([()], key=scores.get)
# while there are nodes unvisited
while unvisited:
# roll back to starting state
state.undo_all()
# get best unvisited node
actions = unvisited.pop()
# roll out actions to get into the intended state
for action in actions:
state.act(action)
# discover all neighbors
for action in state.available_actions:
# do the respective action
state.act(action)
# calculate score and negative amount of actions
scores[(*actions, action)] = eval_function(state), - (len(actions) + 1)
# add this neighbor to the unvisited set
unvisited.add((*actions, action))
# calculate time elapsed
time_elapsed = time.perf_counter() - start_time
# if we reached 175 ms, stop the search
# 25 ms should be enough to finish
if time_elapsed >= 0.175:
# return the best actions so far
best_actions = max(scores, key=scores.get)
# if any direct attack is available, why not?
for remaining_action in state.available_actions:
if remaining_action.type == ActionType.ATTACK \
and remaining_action.target is None:
best_actions = (*best_actions, remaining_action)
return best_actions
# roll back action
state.undo()
# return the actions needed to reach the best node we saw
return max(scores, key=scores.get)
def act_on_draft(network, state):
i = 0
# do a forward pass through all fully connected layers
while f"model/shared_fc{i}/w:0" in network:
weights = network[f"model/shared_fc{i}/w:0"]
biases = network[f"model/shared_fc{i}/b:0"]
state = np.dot(state, weights) + biases
state = network['act_fun'](state)
i += 1
# calculate the policy
pi = np.dot(state, network["model/pi/w:0"]) + network["model/pi/b:0"]
pi = softmax(pi)
# extract the deterministic action
action = np.argmax(pi)
return action
def load_model(path: str):
# read the parameters
with open(base_path + "/" + path, "r") as json_file:
params = json.load(json_file)
# transform to numpy arrays
network = dict((label, np.array(weights)) for label, weights in params.items())
# load activation function for hidden layers
network["act_fun"] = dict(
tanh=np.tanh,
relu=lambda x: np.maximum(x, 0),
elu=lambda x: np.where(x > 0, x, np.exp(x) - 1)
)[params["act_fun"]]
return network
if __name__ == '__main__':
network = None
while True:
# start timer
start_time = time.perf_counter()
# get the input for the turn
game_input = read_game_input()
# if mana is zero then it is draft phase
is_draft_phase = int(game_input[0].split()[1]) == 0
# if network was not loaded, load it
if network is None:
playing_first = game_input[0].split()[2] == game_input[1].split()[2]
path = PATH_FIRST_MODEL if playing_first else PATH_SECOND_MODEL
network = load_model(path)
if is_draft_phase:
state = encode_state(game_input)
action = act_on_draft(network, state)
# print total elapsed time to stderr
print("%.3f ms" % ((time.perf_counter() - start_time) * 1000), file=sys.stderr)
print("PICK", action)
else:
state = State.from_native_input(game_input)
actions = act_on_battle(state)
# print total elapsed time to stderr
print("%.3f ms" % ((time.perf_counter() - start_time) * 1000), file=sys.stderr)
if actions:
print(";".join(map(Action.to_native, actions)))
else:
print("PASS")