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import numpy as np
from math import ceil
from abc import abstractmethod
from src.utils.mymath import a_clip
from src.smb.level import *
defaults = {'n': 5, 'gl': 0.14, 'gg': 0.30, 'wl': 2, 'wg': 10}
class RewardFunc:
def __init__(self, *args):
self.terms = args
self.require_simlt = any(term.require_simlt for term in self.terms)
def get_rewards(self, **kwargs):
return {
term.get_name(): term.compute_rewards(**kwargs)
for term in self.terms
}
def get_n(self):
n = 1
for term in self.terms:
try:
n = max(n, term.n)
except AttributeError:
pass
return n
def __str__(self):
return 'Reward Function:\n' + ',\n'.join('\t' + str(term) for term in self.terms)
class RewardTerm:
def __init__(self, require_simlt):
self.require_simlt = require_simlt
def get_name(self):
return self.__class__.__name__
@abstractmethod
def compute_rewards(self, **kwargs):
pass
class Playability(RewardTerm):
"""
可玩性
"""
def __init__(self, magnitude=1):
super(Playability, self).__init__(True)
self.magnitude=magnitude
def compute_rewards(self, **kwargs):
simlt_res = kwargs['simlt_res']
return [0 if item['playable'] else -self.magnitude for item in simlt_res[1:]]
def __str__(self):
return f'{self.magnitude} * Playability'
class MeanDivergenceFun(RewardTerm):
"""
多样性
"""
def __init__(self, goal_div, n=defaults['n'], s=8):
super().__init__(False)
self.l = goal_div * 0.26 / 0.6
self.u = goal_div * 0.94 / 0.6
self.n = n
self.s = s
def compute_rewards(self, **kwargs):
segs = kwargs['segs']
rewards = []
for i in range(1, len(segs)):
seg = segs[i]
histroy = lvlhcat(segs[max(0, i - self.n): i])
k = 0
divergences = []
while k * self.s <= (min(self.n, i) - 1) * MarioLevel.seg_width:
cmp_seg = histroy[:, k * self.s: k * self.s + MarioLevel.seg_width]
divergences.append(tile_pattern_js_div(seg, cmp_seg))
k += 1
mean_d = sum(divergences) / len(divergences)
if mean_d < self.l:
rewards.append(-(mean_d - self.l) ** 2)
elif mean_d > self.u:
rewards.append(-(mean_d - self.u) ** 2)
else:
rewards.append(0)
return rewards
class SACNovelty(RewardTerm):
def __init__(self, magnitude, goal_div, require_simlt, n):
super().__init__(require_simlt)
self.g = goal_div
self.magnitude = magnitude
self.n = n
def compute_rewards(self, **kwargs):
n_segs = len(kwargs['segs'])
rewards = []
for i in range(1, n_segs):
reward = 0
r_sum = 0
for k in range(1, self.n + 1):
j = i - k
if j < 0:
break
r = 1 - k / (self.n + 1)
r_sum += r
reward += a_clip(self.disim(i, j, **kwargs), self.g, r)
rewards.append(reward * self.magnitude / r_sum)
return rewards
@abstractmethod
def disim(self, i, j, **kwargs):
pass
class LevelSACN(SACNovelty):
def __init__(self, magnitude=1, g=defaults['gl'], w=defaults['wl'], n=defaults['n']):
super(LevelSACN, self).__init__(magnitude, g, False, n)
self.w = w
def disim(self, i, j, **kwargs):
segs = kwargs['segs']
seg1, seg2 = segs[i], segs[j]
return tile_pattern_js_div(seg1, seg2, self.w)
def __str__(self):
s = f'{self.magnitude} * LevelSACN(g={self.g:.3g}, w={self.w}, n={self.n})'
return s
class GameplaySACN(SACNovelty):
def __init__(self, magnitude=1, g=defaults['gg'], w=defaults['wg'], n=defaults['n']):
super(GameplaySACN, self).__init__(magnitude, g, True, n)
self.w = w
def disim(self, i, j, **kwargs):
simlt_res = kwargs['simlt_res']
trace1, trace2 = simlt_res[i]['trace'], simlt_res[j]['trace']
return trace_div(trace1, trace2, self.w)
def __str__(self):
s = f'{self.magnitude} * GameplaySACN(g={self.g:.3g}, w={self.w}, n={self.n})'
return s
class Fun(RewardTerm):
def __init__(self, magnitude=1., num_windows=3, lb=0.26, ub=0.94, stride=8):
super().__init__(False)
self.lb, self.ub = lb, ub
self.magnitude = magnitude
self.stride = stride
self.num_windows = num_windows
self.n = ceil(num_windows * stride / MarioLevel.seg_width - 1e-8)
def compute_rewards(self, **kwargs):
n_segs = len(kwargs['segs'])
lvl = lvlhcat(kwargs['segs'])
W = MarioLevel.seg_width
rewards = []
for i in range(1, n_segs):
seg = lvl[:, W*i: W*(i+1)]
divs = []
for k in range(0, self.num_windows + 1):
s = W * i - k * self.stride
if s < 0:
break
cmp_seg = lvl[:, s:s+W]
divs.append(tile_pattern_kl_div(seg, cmp_seg))
mean_div = np.mean(divs)
rew = 0
if mean_div > self.ub:
rew = -(self.ub - mean_div) ** 2
if mean_div < self.lb:
rew = -(self.lb - mean_div) ** 2
rewards.append(rew * self.magnitude)
return rewards
def __str__(self):
s = f'{self.magnitude} * Fun(lb={self.lb:.2f}, ub={self.ub:.2f}, n={self.num_windows}, stride={self.stride})'
return s
class HistoricalDeviation(RewardTerm):
def __init__(self, magnitude=1., m=3, n=10):
super().__init__(False)
self.magnitude = magnitude
self.m = m
self.n = n
def compute_rewards(self, **kwargs):
segs = kwargs['segs']
n_segs = len(kwargs['segs'])
rewards = []
for i in range(1, n_segs):
divs = []
for k in range(1, self.n+1):
j = i - k
if j < 0:
break
divs.append(tile_pattern_kl_div(segs[i], segs[j]))
divs.sort()
m = min(i, self.m)
rew = np.mean(divs[:m])
rewards.append(rew * self.magnitude)
return rewards
def __str__(self):
return f'{self.magnitude} * HistoricalDeviation(m={self.m}, n={self.n})'
if __name__ == '__main__':
rfunc = HistoricalDeviation()
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