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from kaggle_environments.envs.football.helpers import *
from math import sqrt
from enum import Enum
import torch
import torch.nn as nn
import numpy as np
from ding.torch_utils import tensor_to_list, one_hot, to_ndarray, to_tensor, to_dtype
from ding.utils import MODEL_REGISTRY
from .TamakEriFever.submission import agent
@MODEL_REGISTRY.register('football_kaggle_5th_place')
class FootballKaggle5thPlaceModel(torch.nn.Module):
def __init__(self):
super(FootballKaggle5thPlaceModel, self).__init__()
# be compatiable with bc policy
# to avoid: ValueError: optimizer got an empty parameter list
self._dummy_param = nn.Parameter(torch.zeros(1, 1))
def forward(self, data):
actions = []
data = data['raw_obs']
if isinstance(data['score'], list):
# to be compatiable with collect phase in subprocess mode
data['score'] = torch.stack(data['score'], dim=-1)
# dict of raw observations -> list of dict, each element in the list is the raw obs in a timestep
data = [{k: v[i] for k, v in data.items()} for i in range(data['left_team'].shape[0])]
for d in data:
# the rew obs in one timestep
if isinstance(d['steps_left'], torch.Tensor):
d = {k: v.cpu() for k, v in d.items()}
d = to_ndarray(d)
for k in ['active', 'designated', 'ball_owned_player', 'ball_owned_team']:
d[k] = int(d[k])
for k in ['sticky_actions']:
d[k] = list(d[k])
d = {'controlled_players': 1, 'players_raw': [d]}
actions.append(agent(d)[0])
return {'action': torch.LongTensor(actions), 'logit': one_hot(torch.LongTensor(actions), 19)}
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