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import numpy as np
import torch
import math
from ding.envs.common import EnvElement
from functools import partial
from ding.torch_utils import one_hot
from ding.envs.common import div_func, div_one_hot
N_PLAYER = 11
def score_preprocess(scores):
ret = []
for score in scores:
clip_score = torch.clamp_max(score.unsqueeze(0), 10) # 0-9: 0-9; 10: >=10
ret.append(one_hot(clip_score, num=11).squeeze(0))
return torch.cat(ret, dim=0)
class MatchObs(EnvElement):
_name = "GFootballMatchObs"
def _init(self, cfg):
self._default_val = None
self.template = [
# ------Ball information
{
'key': 'ball',
'ret_key': 'ball_position',
'dim': 3,
'op': lambda x: x,
'value': {
'min': (-1, -0.42, 0),
'max': (1, 0.42, 100),
'dtype': float,
'dinfo': 'float'
},
'other': 'float (x, y, z)'
},
{
'key': 'ball_direction',
'ret_key': 'ball_direction',
'dim': 3,
'op': lambda x: x,
'value': {
'min': (-1, -0.42, 0),
'max': (1, 0.42, 100),
'dtype': float,
'dinfo': 'float'
},
'other': 'float (x, y, z)'
},
{
'key': 'ball_rotation',
'ret_key': 'ball_rotation',
'dim': 3,
'op': lambda x: x,
'value': {
'min': (-math.pi, -math.pi, -math.pi),
'max': (math.pi, math.pi, math.pi),
'dtype': float,
'dinfo': 'float'
},
'other': 'float (x, y, z)'
},
{
'key': 'ball_owned_team',
'ret_key': 'ball_owned_team',
'dim': 3,
'op': lambda x: partial(one_hot, num=3)(x + 1),
'value': {
'min': 0,
'max': 2,
'dtype': float,
'dinfo': 'one-hot'
},
'other': 'one hot 3 value',
'meaning': ['NotOwned', 'LeftTeam', 'RightTeam']
},
{
'key': 'ball_owned_player',
'ret_key': 'ball_owned_player',
'dim': N_PLAYER + 1, # 0...N_1: player_idx, N: nobody
'op': lambda x: partial(one_hot, num=N_PLAYER + 1)(x + N_PLAYER + 1 if x == -1 else x),
'value': {
'min': 0,
'max': 2,
'dtype': float,
'dinfo': 'one-hot'
},
'other': 'one hot 12 value',
'meaning': 'index of player'
},
# ------Controlled player information
{
'key': 'active',
'ret_key': 'active_player',
'dim': N_PLAYER,
'op': partial(one_hot, num=N_PLAYER),
'value': {
'min': 0,
'max': 2,
'dtype': float,
'dinfo': 'one-hot'
},
'other': 'one hot 11 value',
'meaning': 'index of controlled player'
},
{
'key': 'designated', # In non-multiagent mode it is always equal to `active`
'ret_key': 'designated_player',
'dim': N_PLAYER,
'op': partial(one_hot, num=N_PLAYER),
'value': {
'min': 0,
'max': 2,
'dtype': float,
'dinfo': 'one-hot'
},
'other': 'one hot 11 value',
'meaning': 'index of player'
},
{
'key': 'sticky_actions',
'ret_key': 'active_player_sticky_actions',
'dim': 10,
'op': lambda x: x,
'value': {
'min': 0,
'max': 2,
'dtype': float,
'dinfo': 'boolean vector'
},
'other': 'boolean vector with 10 value',
'meaning': [
'Left', 'TopLeft', 'Top', 'TopRight', 'Right', 'BottomRight', 'Bottom', 'BottomLeft', 'Sprint',
'Dribble'
] # 8 directions are one-hot
},
# ------Match state
{
'key': 'score',
'ret_key': 'score',
'dim': 22,
'op': score_preprocess,
'value': {
'min': 0,
'max': 2,
'dtype': float,
'dinfo': 'one-hot'
},
'other': 'each score one hot 11 values(10 for 0-9, 1 for over 10), concat two scores',
},
{
'key': 'steps_left',
'ret_key': 'steps_left',
'dim': 30,
'op': partial(div_one_hot, max_val=2999, ratio=100),
'value': {
'min': 0,
'max': 2,
'dtype': float,
'dinfo': 'one-hot'
},
'other': 'div(50), one hot 30 values',
},
{
'key': 'game_mode',
'ret_key': 'game_mode',
'dim': 7,
'op': partial(one_hot, num=7),
'value': {
'min': 0,
'max': 2,
'dtype': float,
'dinfo': 'one-hot'
},
'other': 'one-hot 7 values',
'meaning': ['Normal', 'KickOff', 'GoalKick', 'FreeKick', 'Corner', 'ThrowIn', 'Penalty']
},
]
self.cfg = cfg
self._shape = {t['key']: t['dim'] for t in self.template}
self._value = {t['key']: t['value'] for t in self.template}
self._to_agent_processor = self.parse
self._from_agent_processor = None
def parse(self, obs: dict) -> dict:
'''
Overview: find corresponding setting in cfg, parse the feature
Arguments:
- feature (:obj:`ndarray`): the feature to parse
- idx_dict (:obj:`dict`): feature index dict
Returns:
- ret (:obj:`list`): parse result tensor list
'''
ret = {}
for item in self.template:
key = item['key']
ret_key = item['ret_key']
data = obs[key]
if not isinstance(data, list):
data = [data]
data = torch.Tensor(data) if item['value']['dinfo'] != 'one-hot' else torch.LongTensor(data)
try:
data = item['op'](data)
except RuntimeError:
print(item, data)
raise RuntimeError
if len(data.shape) == 2:
data = data.squeeze(0)
ret[ret_key] = data.numpy()
return ret
def _details(self):
return 'Match Global Obs: Ball, Controlled Player and Match State'
class PlayerObs(EnvElement):
_name = "GFootballPlayerObs"
def _init(self, cfg):
self._default_val = None
self.template = [
{
'key': 'team',
'ret_key': 'team',
'dim': 2,
'op': partial(one_hot, num=2), # 0 for left, 1 for right
'value': {
'min': 0,
'max': 2,
'dtype': float,
'dinfo': 'one-hot'
},
'other': 'one-hot 2 values for which team'
},
{
'key': 'index',
'ret_key': 'index',
'dim': N_PLAYER,
'op': partial(one_hot, num=N_PLAYER),
'value': {
'min': 0,
'max': N_PLAYER,
'dtype': float,
'dinfo': 'one-hot'
},
'other': 'one-hot N_PLAYER values for index in one team'
},
{
'key': 'position',
'ret_key': 'position',
'dim': 2,
'op': lambda x: x,
'value': {
'min': (-1, -0.42),
'max': (1, 0.42),
'dtype': float,
'dinfo': 'float'
},
'other': 'float (x, y)'
},
{
'key': 'direction',
'ret_key': 'direction',
'dim': 2,
'op': lambda x: x,
'value': {
'min': (-1, -0.42),
'max': (1, 0.42),
'dtype': float,
'dinfo': 'float'
},
'other': 'float'
},
{
'key': 'tired_factor',
'ret_key': 'tired_factor',
'dim': 1,
'op': lambda x: x,
'value': {
'min': (0, ),
'max': (1, ),
'dtype': float,
'dinfo': 'float'
},
'other': 'float'
},
{
'key': 'yellow_card',
'ret_key': 'yellow_card',
'dim': 2,
'op': partial(one_hot, num=2),
'value': {
'min': 0,
'max': 2,
'dtype': float,
'dinfo': 'one-hot'
},
'other': 'one hot 2 values'
},
{
'key': 'active', # 0(False) means got a red card
'ret_key': 'active',
'dim': 2,
'op': partial(one_hot, num=2),
'value': {
'min': 0,
'max': 2,
'dtype': float,
'dinfo': 'one-hot'
},
'other': 'float'
},
{
'key': 'roles',
'ret_key': 'role',
'dim': 10,
'op': partial(one_hot, num=10),
'value': {
'min': 0,
'max': 2,
'dtype': float,
'dinfo': 'one-hot'
},
'other': 'one-hot 10 values',
'meaning': [
'GoalKeeper', 'CentreBack', 'LeftBack', 'RightBack', 'DefenceMidfield', 'CentralMidfield',
'LeftMidfield', 'RightMidfield', 'AttackMidfield', 'CentralFront'
]
},
]
self.cfg = cfg
self._shape = {'players': {t['key']: t['dim'] for t in self.template}}
self._value = {'players': {t['key']: t['value'] for t in self.template}}
self._to_agent_processor = self.parse
self._from_agent_processor = None
def parse(self, obs: dict) -> dict:
players = []
for player_idx in range(N_PLAYER):
players.append(self._parse(obs, 'left_team', player_idx))
for player_idx in range(N_PLAYER):
players.append(self._parse(obs, 'right_team', player_idx))
return {'players': players}
def _parse(self, obs: dict, left_right: str, player_idx) -> dict:
player_dict = {
'team': 0 if left_right == 'left_team' else 1,
'index': player_idx,
}
for item in self.template:
key = item['key']
ret_key = item['ret_key']
if key in ['team', 'index']:
data = player_dict[key]
elif key == 'position':
player_stat = left_right
data = obs[player_stat][player_idx]
else:
player_stat = left_right + '_' + key
data = obs[player_stat][player_idx]
if not isinstance(data, np.ndarray):
data = [data]
data = torch.Tensor(data) if item['value']['dinfo'] != 'one-hot' else torch.LongTensor(data)
try:
data = item['op'](data)
except RuntimeError:
print(item, data)
raise RuntimeError
if len(data.shape) == 2:
data = data.squeeze(0)
player_dict[ret_key] = data.numpy()
return player_dict
def _details(self):
return 'Single Player Obs'
class FullObs(EnvElement):
_name = "GFootballFullObs"
def _init(self, cfg):
self._default_val = None
self.template = [
{
'key': 'player',
'ret_key': 'player',
'dim': 36,
'op': lambda x: x,
'value': {
'min': (
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -0.42, -1, -0.42, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0
),
'max': (
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.42, 1, 0.42, float(np.inf), 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1
),
'dtype': float,
'dinfo': 'mix'
},
'other': 'mixed active player info'
},
{
'key': 'ball',
'ret_key': 'ball',
'dim': 18,
'op': lambda x: x,
'value': {
'min': (-1, -0.42, 0, 0, 0, 0, 0, 0, 0, -2, -0.84, -20, -8.4, 0, 0, 0, 0, 0),
'max': (1, 0.42, 100, 1, 1, 1, 1, 1, 1, 2, 0.84, 20, 8.4, np.inf, np.inf, 2.5, 1, 1),
'dtype': float,
'dinfo': 'mix'
},
'other': 'mixed ball info, relative to active player'
},
{
'key': 'LeftTeam',
'ret_key': 'LeftTeam',
'dim': 7,
'op': lambda x: x,
'value': {
'min': (-1, -0.42, -1, -0.42, 0, 0, 0),
'max': (1, 0.42, 1, 0.42, 100, 2.5, 1),
'dtype': float,
'dinfo': 'mix'
},
'other': 'mixed player info, relative to active player,\
will have 10+1 infos(all left team member and closest member )'
},
{
'key': 'RightTeam',
'ret_key': 'RightTeam',
'dim': 7,
'op': lambda x: x,
'value': {
'min': (-1, -0.42, -1, -0.42, 0, 0, 0),
'max': (1, 0.42, 1, 0.42, 100, 2.5, 1),
'dtype': float,
'dinfo': 'mix'
},
'other': 'mixed player info, relative to active player,\
will have 10+1 infos(all right team member and closest member )'
},
]
self.cfg = cfg
self._shape = {t['key']: t['dim'] for t in self.template}
self._value = {t['key']: t['value'] for t in self.template}
def _details(self):
return 'Full Obs for Gfootball Self Play'
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