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reworks loss function and training data, cleans up directions in code

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zomseffen 2022-11-14 11:17:00 +01:00
parent cf4d773c10
commit 26e7ffb12b
2 changed files with 129 additions and 175 deletions
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31
labirinth_ai/Models/BaseModel.py
View file

@ -1,7 +1,7 @@
import torch import torch
from torch import nn from torch import nn
import numpy as np import numpy as np
import tqdm from tqdm import tqdm
from torch.utils.data import Dataset, DataLoader from torch.utils.data import Dataset, DataLoader
import os import os
@ -14,6 +14,7 @@ print(f"Using {device} device")
# Define model # Define model
class BaseModel(nn.Module): class BaseModel(nn.Module):
evolutionary = False evolutionary = False
def __init__(self, view_dimension, action_num, channels): def __init__(self, view_dimension, action_num, channels):
super(BaseModel, self).__init__() super(BaseModel, self).__init__()
self.flatten = nn.Flatten() self.flatten = nn.Flatten()
@ -59,11 +60,18 @@ def create_optimizer(model):
def create_loss_function(action): def create_loss_function(action):
lambda_factor = 0.0
split_factor = 1.0
def custom_loss(prediction, target): def custom_loss(prediction, target):
return torch.mean(0.5 * torch.square( return torch.mean(split_factor * torch.square(
0.1 * target[:, 0, 0] + target[:, 1, 0] - ( # discounted best estimate the old weights made for t+1
prediction[:, action, 0] + prediction[:, action, 1])) + 0.5 * torch.square( lambda_factor * target[:, 0, 0] +
target[:, 1, 0] - prediction[:, action, 0]), dim=0) # actual reward for t
target[:, 1, 0] -
# estimate for current weights
(prediction[:, action, 0] + prediction[:, action, 1])) +
# trying to learn present reward separate from future reward
(1.0 - split_factor) * torch.square(target[:, 1, 0] - prediction[:, action, 0]), dim=0)
return custom_loss return custom_loss
@ -75,11 +83,16 @@ def from_numpy(x):
def train(states, targets, model, optimizer): def train(states, targets, model, optimizer):
for action in range(model.action_num): for action in range(model.action_num):
data_set = BaseDataSet(states[action], targets[action]) data_set = BaseDataSet(states[action], targets[action])
dataloader = DataLoader(data_set, batch_size=64, shuffle=True) dataloader = DataLoader(data_set, batch_size=256, shuffle=True)
loss_fn = create_loss_function(action) loss_fn = create_loss_function(action)
size = len(dataloader) size = len(dataloader)
model.train() model.train()
epochs = 1
with tqdm(range(epochs)) as progress_bar:
for _ in enumerate(progress_bar):
losses = []
for batch, (X, y) in enumerate(dataloader): for batch, (X, y) in enumerate(dataloader):
X, y = X.to(device), y.to(device) X, y = X.to(device), y.to(device)
@ -92,9 +105,9 @@ def train(states, targets, model, optimizer):
loss.backward(retain_graph=True) loss.backward(retain_graph=True)
optimizer.step() optimizer.step()
if batch % 100 == 0: losses.append(loss.item())
loss, current = loss.item(), batch * len(X) progress_bar.set_postfix(loss=np.average(losses))
print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]") progress_bar.update()
model.eval() model.eval()
del data_set del data_set

245
labirinth_ai/Subject.py
View file

@ -108,34 +108,6 @@ class QLearner(Subject):
def createState(self, world: LabyrinthWorld): def createState(self, world: LabyrinthWorld):
state = np.zeros((2 * self.viewD + 1, 2 * self.viewD + 1), np.int) # - 1 state = np.zeros((2 * self.viewD + 1, 2 * self.viewD + 1), np.int) # - 1
# # floodfill state
# queued = [(0, 0)]
# todo = [(0, 0, 0)]
# while todo != []:
# doing = todo.pop(0)
#
# if self.x + doing[0] >= 0 and self.x + doing[0] < 64 and self.y + doing[1] >= 0 and self.y + doing[1] < 64:
# value = world.board[self.x + doing[0], self.y + doing[1]]
# state[self.viewD + doing[0], self.viewD + doing[1]] = value
#
# # if value == 3:
# # state[self.viewD + doing[0], self.viewD + doing[1]] = value
#
# if value != 0 and doing[2] < self.viewD:
# for i in range(-1, 2, 1):
# for j in range(-1, 2, 1):
# # 4-neighbour. without it it is 8-neighbour
# if abs(i) + abs(j) == 1:
# if (doing[0] + i, doing[1] + j) not in queued:
# queued.append((doing[0] + i, doing[1] + j))
# todo.append((doing[0] + i, doing[1] + j, doing[2] + 1))
#
# for sub in world.subjects:
# if (sub.x - self.x, sub.y - self.y) in queued and state[
# self.viewD + sub.x - self.x, self.viewD + sub.y - self.y] != 3:
# state[self.viewD + sub.x - self.x, self.viewD + sub.y - self.y] = state[
# self.viewD + sub.x - self.x, self.viewD + sub.y - self.y] * 100 + sub.col
maxdirleft = self.x - max(self.x - (self.viewD), 0) maxdirleft = self.x - max(self.x - (self.viewD), 0)
maxdirright = min(self.x + (self.viewD), (world.board_shape[0] - 1)) - self.x maxdirright = min(self.x + (self.viewD), (world.board_shape[0] - 1)) - self.x
maxdirup = self.y - max(self.y - (self.viewD), 0) maxdirup = self.y - max(self.y - (self.viewD), 0)
@ -300,6 +272,7 @@ class DoubleQLearner(QLearner):
pass pass
RECALCULATE = False
class NetLearner(Subject): class NetLearner(Subject):
right = (1, 0) right = (1, 0)
left = (-1, 0) left = (-1, 0)
@ -440,7 +413,6 @@ class NetLearner(Subject):
axs[1, 1].set_title('grass') axs[1, 1].set_title('grass')
plt.show(block=True) plt.show(block=True)
def createState(self, world: LabyrinthWorld): def createState(self, world: LabyrinthWorld):
state = np.zeros((2 * self.viewD + 1, 2 * self.viewD + 1), np.float) # - 1 state = np.zeros((2 * self.viewD + 1, 2 * self.viewD + 1), np.float) # - 1
state2 = np.zeros((2 * self.viewD + 1, 2 * self.viewD + 1), np.float) # - 1 state2 = np.zeros((2 * self.viewD + 1, 2 * self.viewD + 1), np.float) # - 1
@ -474,10 +446,8 @@ class NetLearner(Subject):
action = self.lastAction action = self.lastAction
return np.reshape(np.concatenate((area, action)), (1, 4 * (2 * self.viewD + 1) * (2 * self.viewD + 1) + 2)) return np.reshape(np.concatenate((area, action)), (1, 4 * (2 * self.viewD + 1) * (2 * self.viewD + 1) + 2))
def calculateAction(self, world: LabyrinthWorld, vals=None, state=None): def generate_valid_directions(self, world: LabyrinthWorld):
# 0, 0 is top left
directions = [] directions = []
if self.x - 1 >= 0: if self.x - 1 >= 0:
if world.board[self.x - 1, self.y] != 0: if world.board[self.x - 1, self.y] != 0:
directions.append(self.left) directions.append(self.left)
@ -493,9 +463,15 @@ class NetLearner(Subject):
if self.y + 1 < world.board_shape[1]: if self.y + 1 < world.board_shape[1]:
if world.board[self.x, self.y + 1] != 0: if world.board[self.x, self.y + 1] != 0:
directions.append(self.down) directions.append(self.down)
return directions
def calculateAction(self, world: LabyrinthWorld, vals=None, state=None):
# 0, 0 is top left
directions = self.generate_valid_directions(world)
if directions == []: if directions == []:
print('Wut?') print('Wut?')
return
if directions != [] and self.alive: if directions != [] and self.alive:
if state is None: if state is None:
@ -550,6 +526,7 @@ class NetLearner(Subject):
self.nextTrain = min(self.batchsize + self.nextTrain, (self.historySizeMul + 1) * self.batchsize) self.nextTrain = min(self.batchsize + self.nextTrain, (self.historySizeMul + 1) * self.batchsize)
print(len(self.samples), self.nextTrain) print(len(self.samples), self.nextTrain)
if not self.random:
self.accumulated_rewards += self.lastReward self.accumulated_rewards += self.lastReward
self.lastAction = self.action self.lastAction = self.action
@ -562,27 +539,10 @@ class NetLearner(Subject):
self.executeAction(world, self.action) self.executeAction(world, self.action)
def randomAct(self, world: LabyrinthWorld): def randomAct(self, world: LabyrinthWorld):
right = (1, 0) directions = self.generate_valid_directions(world)
left = (-1, 0)
up = (0, -1)
down = (0, 1)
directions = []
if self.x - 1 >= 0: if len(directions) == 0:
if world.board[self.x - 1, self.y] != 0: return 0, 0
directions.append(left)
if self.x + 1 < world.board_shape[0]:
if world.board[self.x + 1, self.y] != 0:
directions.append(right)
if self.y - 1 >= 0:
if world.board[self.x, self.y - 1] != 0:
directions.append(up)
if self.y + 1 < world.board_shape[1]:
if world.board[self.x, self.y + 1] != 0:
directions.append(down)
d = random.randint(0, len(directions) - 1) d = random.randint(0, len(directions) - 1)
action = directions[d] action = directions[d]
@ -616,16 +576,16 @@ class NetLearner(Subject):
replace=True) replace=True)
samples = samples[index] samples = samples[index]
# self.samples = [] # self.samples = []
target[:, 1, 0] = samples[:, 0, 3] # reward t-2 got
if partTwo: if partTwo:
target[:, 1, 0] = samples[:, 1, 3] #reward t-2 got if RECALCULATE:
nextState = np.concatenate(samples[:, 1, 0]) #states of t-1 nextState = np.concatenate(samples[:, 1, 0]) #states of t-1
nextVals = self.model(from_numpy(nextState)).detach().numpy() nextVals = self.model(from_numpy(nextState)).detach().numpy()
nextVals2 = nextVals[:, i, 0] + nextVals[:, i, 1] nextVals2 = np.max(nextVals[:, :, 0] + nextVals[:, :, 1], axis=1)
target[:, 0, 0] = nextVals2 #best q t-1 target[:, 0, 0] = nextVals2 #best q t-1
else: else:
target[:, 1, 0] = np.array(list(map(lambda elem: list(elem), list(np.array(samples[:, 1, 4])))))[:, i] # reward t-2 got target[:, 0, 0] = samples[:, 1, 2] #best q t-1
targets.append(target) targets.append(target)
@ -697,27 +657,7 @@ class Herbivore(NetLearner):
return np.reshape(np.concatenate((area, action)), (1, 4 * (2 * self.viewD + 1) * (2 * self.viewD + 1) + 2)) return np.reshape(np.concatenate((area, action)), (1, 4 * (2 * self.viewD + 1) * (2 * self.viewD + 1) + 2))
def executeAction(self, world: LabyrinthWorld, action): def executeAction(self, world: LabyrinthWorld, action):
right = (1, 0) directions = self.generate_valid_directions(world)
left = (-1, 0)
up = (0, -1)
down = (0, 1)
directions = []
if self.x - 1 >= 0:
if world.board[self.x - 1, self.y] != 0:
directions.append(left)
if self.x + 1 < world.board_shape[0]:
if world.board[self.x + 1, self.y] != 0:
directions.append(right)
if self.y - 1 >= 0:
if world.board[self.x, self.y - 1] != 0:
directions.append(up)
if self.y + 1 < world.board_shape[1]:
if world.board[self.x, self.y + 1] != 0:
directions.append(down)
if len(action) == 2: if len(action) == 2:
if len(world.subjectDict[(self.x + action[0], self.y + action[1])]) > 0: if len(world.subjectDict[(self.x + action[0], self.y + action[1])]) > 0:
for sub in world.subjectDict[(self.x + action[0], self.y + action[1])]: for sub in world.subjectDict[(self.x + action[0], self.y + action[1])]:
@ -729,26 +669,26 @@ class Herbivore(NetLearner):
self.alive = False self.alive = False
self.lastRewards = [] self.lastRewards = []
if right in directions: if self.right in directions:
self.lastRewards.append(world.grass[self.x + 1, self.y]) self.lastRewards.append(world.grass[self.x + 1, self.y])
else: else:
self.lastRewards.append(0) self.lastRewards.append(0)
if left in directions: if self.left in directions:
self.lastRewards.append(world.grass[self.x - 1, self.y]) self.lastRewards.append(world.grass[self.x - 1, self.y])
else: else:
self.lastRewards.append(0) self.lastRewards.append(0)
if up in directions: if self.up in directions:
self.lastRewards.append(world.grass[self.x, self.y - 1]) self.lastRewards.append(world.grass[self.x, self.y - 1])
else: else:
self.lastRewards.append(0) self.lastRewards.append(0)
if down in directions: if self.down in directions:
self.lastRewards.append(world.grass[self.x, self.y + 1]) self.lastRewards.append(world.grass[self.x, self.y + 1])
else: else:
self.lastRewards.append(0) self.lastRewards.append(0)
assert len(self.lastRewards) == 4, 'Last Rewards not filled correctly!' assert len(self.lastRewards) == 4, 'Last Rewards not filled correctly!'
world.subjectDict[(self.x, self.y)].remove(self) world.subjectDict[(self.x, self.y)].remove(self)
self.lastReward += world.trailMix[self.x, self.y] # self.lastReward += world.trailMix[self.x, self.y]
self.x += action[0] self.x += action[0]
self.y += action[1] self.y += action[1]
world.subjectDict[(self.x, self.y)].append(self) world.subjectDict[(self.x, self.y)].append(self)
@ -757,33 +697,58 @@ class Herbivore(NetLearner):
world.grass[self.x, self.y] = 0 world.grass[self.x, self.y] = 0
world.hunter_grass[self.x, self.y] = 0 world.hunter_grass[self.x, self.y] = 0
def generate_valid_directions(self, world: LabyrinthWorld):
directions = []
if self.x - 1 >= 0:
if world.board[self.x - 1, self.y] != 0:
if not world.subjectDict[(self.x - 1, self.y)]:
directions.append(self.left)
if self.x + 1 < world.board_shape[0]:
if world.board[self.x + 1, self.y] != 0:
if not world.subjectDict[(self.x + 1, self.y)]:
directions.append(self.right)
if self.y - 1 >= 0:
if world.board[self.x, self.y - 1] != 0:
if not world.subjectDict[(self.x, self.y - 1)]:
directions.append(self.up)
if self.y + 1 < world.board_shape[1]:
if world.board[self.x, self.y + 1] != 0:
if not world.subjectDict[(self.x, self.y + 1)]:
directions.append(self.down)
return directions
def randomAct(self, world: LabyrinthWorld): def randomAct(self, world: LabyrinthWorld):
right = (1, 0)
left = (-1, 0)
up = (0, -1)
down = (0, 1)
directions = [] directions = []
actDict = {} actDict = {}
if self.x - 1 >= 0: if self.x - 1 >= 0:
if world.board[self.x - 1, self.y] != 0: if world.board[self.x - 1, self.y] != 0:
directions.append(left) if not world.subjectDict[(self.x - 1, self.y)]:
actDict[left] = world.grass[self.x - 1, self.y] directions.append(self.left)
actDict[self.left] = world.grass[self.x - 1, self.y]
if self.x + 1 < world.board_shape[0]: if self.x + 1 < world.board_shape[0]:
if world.board[self.x + 1, self.y] != 0: if world.board[self.x + 1, self.y] != 0:
directions.append(right) if not world.subjectDict[(self.x + 1, self.y)]:
actDict[right] = world.grass[self.x + 1, self.y] directions.append(self.right)
actDict[self.right] = world.grass[self.x + 1, self.y]
if self.y - 1 >= 0: if self.y - 1 >= 0:
if world.board[self.x, self.y - 1] != 0: if world.board[self.x, self.y - 1] != 0:
directions.append(up) if not world.subjectDict[(self.x, self.y - 1)]:
actDict[up] = world.grass[self.x, self.y - 1] directions.append(self.up)
actDict[self.up] = world.grass[self.x, self.y - 1]
if self.y + 1 < world.board_shape[1]: if self.y + 1 < world.board_shape[1]:
if world.board[self.x, self.y + 1] != 0: if world.board[self.x, self.y + 1] != 0:
directions.append(down) if not world.subjectDict[(self.x, self.y + 1)]:
actDict[down] = world.grass[self.x, self.y + 1] directions.append(self.down)
actDict[self.down] = world.grass[self.x, self.y + 1]
if len(directions) == 0:
return 0, 0
allowedActions = dict(filter(lambda elem: elem[0] in directions, actDict.items())) allowedActions = dict(filter(lambda elem: elem[0] in directions, actDict.items()))
action = max(allowedActions, key=allowedActions.get) action = max(allowedActions, key=allowedActions.get)
@ -792,7 +757,7 @@ class Herbivore(NetLearner):
def respawnUpdate(self, x, y, world: LabyrinthWorld): def respawnUpdate(self, x, y, world: LabyrinthWorld):
super(Herbivore, self).respawnUpdate(x, y, world) super(Herbivore, self).respawnUpdate(x, y, world)
self.lastReward -= 1 # self.lastReward -= 1
class Hunter(NetLearner): class Hunter(NetLearner):
@ -802,16 +767,12 @@ class Hunter(NetLearner):
g = 255 g = 255
b = 255 b = 255
def randomAct(self, world: LabyrinthWorld): def randomAct(self, world: LabyrinthWorld):
right = (1, 0)
left = (-1, 0)
up = (0, -1)
down = (0, 1)
directions = [] directions = []
actDict = {} actDict = {}
if self.x - 1 >= 0: if self.x - 1 >= 0:
if world.board[self.x - 1, self.y] > 0.01: if world.board[self.x - 1, self.y] > 0.01:
directions.append(left) directions.append(self.left)
sub = self.getClosestSubject(world, self.x - 1, self.y) sub = self.getClosestSubject(world, self.x - 1, self.y)
dist = self.viewD dist = self.viewD
@ -819,15 +780,15 @@ class Hunter(NetLearner):
dist = np.sqrt(np.square(self.x - 1 - sub.x) + np.square(self.y - sub.y)) dist = np.sqrt(np.square(self.x - 1 - sub.x) + np.square(self.y - sub.y))
distReward = self.viewD - dist distReward = self.viewD - dist
actDict[left] = world.trailMix[self.x - 1, self.y] + world.hunter_grass[self.x - 1, self.y] * self.hunterGrassScale + distReward actDict[self.left] = world.trailMix[self.x - 1, self.y] + world.hunter_grass[self.x - 1, self.y] * self.hunterGrassScale + distReward
if len(world.subjectDict[(self.x + left[0], self.y + left[1])]) > 0: if len(world.subjectDict[(self.x + self.left[0], self.y + self.left[1])]) > 0:
for sub in world.subjectDict[(self.x + left[0], self.y + left[1])]: for sub in world.subjectDict[(self.x + self.left[0], self.y + self.left[1])]:
if sub.col != self.col: if sub.col != self.col:
actDict[left] += 10 actDict[self.left] += 10
if self.x + 1 < world.board_shape[0]: if self.x + 1 < world.board_shape[0]:
if world.board[self.x + 1, self.y] > 0.01: if world.board[self.x + 1, self.y] > 0.01:
directions.append(right) directions.append(self.right)
sub = self.getClosestSubject(world, self.x + 1, self.y) sub = self.getClosestSubject(world, self.x + 1, self.y)
dist = self.viewD dist = self.viewD
@ -835,15 +796,15 @@ class Hunter(NetLearner):
dist = np.sqrt(np.square(self.x + 1 - sub.x) + np.square(self.y - sub.y)) dist = np.sqrt(np.square(self.x + 1 - sub.x) + np.square(self.y - sub.y))
distReward = self.viewD - dist distReward = self.viewD - dist
actDict[right] = world.trailMix[self.x + 1, self.y] + world.hunter_grass[self.x + 1, self.y] * self.hunterGrassScale + distReward actDict[self.right] = world.trailMix[self.x + 1, self.y] + world.hunter_grass[self.x + 1, self.y] * self.hunterGrassScale + distReward
if len(world.subjectDict[(self.x + right[0], self.y + right[1])]) > 0: if len(world.subjectDict[(self.x + self.right[0], self.y + self.right[1])]) > 0:
for sub in world.subjectDict[(self.x + right[0], self.y + right[1])]: for sub in world.subjectDict[(self.x + self.right[0], self.y + self.right[1])]:
if sub.col != self.col: if sub.col != self.col:
actDict[right] += 10 actDict[self.right] += 10
if self.y - 1 >= 0: if self.y - 1 >= 0:
if world.board[self.x, self.y - 1] > 0.01: if world.board[self.x, self.y - 1] > 0.01:
directions.append(up) directions.append(self.up)
sub = self.getClosestSubject(world, self.x, self.y - 1) sub = self.getClosestSubject(world, self.x, self.y - 1)
dist = self.viewD dist = self.viewD
@ -851,15 +812,15 @@ class Hunter(NetLearner):
dist = np.sqrt(np.square(self.x - sub.x) + np.square(self.y - 1 - sub.y)) dist = np.sqrt(np.square(self.x - sub.x) + np.square(self.y - 1 - sub.y))
distReward = self.viewD - dist distReward = self.viewD - dist
actDict[up] = world.trailMix[self.x, self.y - 1] + world.hunter_grass[self.x, self.y - 1] * self.hunterGrassScale + distReward actDict[self.up] = world.trailMix[self.x, self.y - 1] + world.hunter_grass[self.x, self.y - 1] * self.hunterGrassScale + distReward
if len(world.subjectDict[(self.x + up[0], self.y + up[1])]) > 0: if len(world.subjectDict[(self.x + self.up[0], self.y + self.up[1])]) > 0:
for sub in world.subjectDict[(self.x + up[0], self.y + up[1])]: for sub in world.subjectDict[(self.x + self.up[0], self.y + self.up[1])]:
if sub.col != self.col: if sub.col != self.col:
actDict[up] += 10 actDict[self.up] += 10
if self.y + 1 < world.board_shape[1]: if self.y + 1 < world.board_shape[1]:
if world.board[self.x, self.y + 1] > 0.01: if world.board[self.x, self.y + 1] > 0.01:
directions.append(down) directions.append(self.down)
sub = self.getClosestSubject(world, self.x, self.y + 1) sub = self.getClosestSubject(world, self.x, self.y + 1)
dist = self.viewD dist = self.viewD
@ -867,11 +828,11 @@ class Hunter(NetLearner):
dist = np.sqrt(np.square(self.x - sub.x) + np.square(self.y + 1 - sub.y)) dist = np.sqrt(np.square(self.x - sub.x) + np.square(self.y + 1 - sub.y))
distReward = self.viewD - dist distReward = self.viewD - dist
actDict[down] = world.trailMix[self.x, self.y + 1] + world.hunter_grass[self.x, self.y + 1] * self.hunterGrassScale + distReward actDict[self.down] = world.trailMix[self.x, self.y + 1] + world.hunter_grass[self.x, self.y + 1] * self.hunterGrassScale + distReward
if len(world.subjectDict[(self.x + down[0], self.y + down[1])]) > 0: if len(world.subjectDict[(self.x + self.down[0], self.y + self.down[1])]) > 0:
for sub in world.subjectDict[(self.x + down[0], self.y + down[1])]: for sub in world.subjectDict[(self.x + self.down[0], self.y + self.down[1])]:
if sub.col != self.col: if sub.col != self.col:
actDict[down] += 10 actDict[self.down] += 10
if len(actDict) > 0: if len(actDict) > 0:
allowedActions = dict(filter(lambda elem: elem[0] in directions, actDict.items())) allowedActions = dict(filter(lambda elem: elem[0] in directions, actDict.items()))
@ -919,47 +880,27 @@ class Hunter(NetLearner):
def executeAction(self, world: LabyrinthWorld, action): def executeAction(self, world: LabyrinthWorld, action):
grass_factor = 0.5 grass_factor = 0.5
right = (1, 0) directions = self.generate_valid_directions(world)
left = (-1, 0)
up = (0, -1)
down = (0, 1)
directions = []
if self.x - 1 >= 0:
if world.board[self.x - 1, self.y] != 0:
directions.append(left)
if self.x + 1 < world.board_shape[0]:
if world.board[self.x + 1, self.y] != 0:
directions.append(right)
if self.y - 1 >= 0:
if world.board[self.x, self.y - 1] != 0:
directions.append(up)
if self.y + 1 < world.board_shape[1]:
if world.board[self.x, self.y + 1] != 0:
directions.append(down)
if len(action) == 2: if len(action) == 2:
right_kill = left_kill = up_kill = down_kill = False right_kill = left_kill = up_kill = down_kill = False
if right in directions: if self.right in directions:
for sub in world.subjectDict[(self.x + action[0], self.y + action[1])]: for sub in world.subjectDict[(self.x + self.right[0], self.y + self.right[1])]:
if sub.alive: if sub.alive:
if sub.col != self.col: if sub.col != self.col:
right_kill = True right_kill = True
if left in directions: if self.left in directions:
for sub in world.subjectDict[(self.x + left[0], self.y + left[1])]: for sub in world.subjectDict[(self.x + self.left[0], self.y + self.left[1])]:
if sub.alive: if sub.alive:
if sub.col != self.col: if sub.col != self.col:
left_kill = True left_kill = True
if up in directions: if self.up in directions:
for sub in world.subjectDict[(self.x + up[0], self.y + up[1])]: for sub in world.subjectDict[(self.x + self.up[0], self.y + self.up[1])]:
if sub.alive: if sub.alive:
if sub.col != self.col: if sub.col != self.col:
up_kill = True up_kill = True
if down in directions: if self.down in directions:
for sub in world.subjectDict[(self.x + down[0], self.y + down[1])]: for sub in world.subjectDict[(self.x + self.down[0], self.y + self.down[1])]:
if sub.alive: if sub.alive:
if sub.col != self.col: if sub.col != self.col:
down_kill = True down_kill = True
@ -974,7 +915,7 @@ class Hunter(NetLearner):
self.alive = True self.alive = True
self.lastRewards = [] self.lastRewards = []
if right in directions: if self.right in directions:
sub = self.getClosestSubject(world, self.x + 1, self.y) sub = self.getClosestSubject(world, self.x + 1, self.y)
dist = self.viewD dist = self.viewD
if sub is not None: if sub is not None:
@ -986,7 +927,7 @@ class Hunter(NetLearner):
self.lastRewards.append(world.trailMix[self.x + 1, self.y] + world.hunter_grass[self.x + 1, self.y] * grass_factor + distReward) self.lastRewards.append(world.trailMix[self.x + 1, self.y] + world.hunter_grass[self.x + 1, self.y] * grass_factor + distReward)
else: else:
self.lastRewards.append(0) self.lastRewards.append(0)
if left in directions: if self.left in directions:
sub = self.getClosestSubject(world, self.x - 1, self.y) sub = self.getClosestSubject(world, self.x - 1, self.y)
dist = self.viewD dist = self.viewD
if sub is not None: if sub is not None:
@ -998,7 +939,7 @@ class Hunter(NetLearner):
self.lastRewards.append(world.trailMix[self.x - 1, self.y] + world.hunter_grass[self.x - 1, self.y] * grass_factor + distReward) self.lastRewards.append(world.trailMix[self.x - 1, self.y] + world.hunter_grass[self.x - 1, self.y] * grass_factor + distReward)
else: else:
self.lastRewards.append(0) self.lastRewards.append(0)
if up in directions: if self.up in directions:
sub = self.getClosestSubject(world, self.x, self.y - 1) sub = self.getClosestSubject(world, self.x, self.y - 1)
dist = self.viewD dist = self.viewD
if sub is not None: if sub is not None:
@ -1010,7 +951,7 @@ class Hunter(NetLearner):
self.lastRewards.append(world.trailMix[self.x, self.y - 1] + world.hunter_grass[self.x, self.y - 1] * grass_factor + distReward) self.lastRewards.append(world.trailMix[self.x, self.y - 1] + world.hunter_grass[self.x, self.y - 1] * grass_factor + distReward)
else: else:
self.lastRewards.append(0) self.lastRewards.append(0)
if down in directions: if self.down in directions:
sub = self.getClosestSubject(world, self.x, self.y + 1) sub = self.getClosestSubject(world, self.x, self.y + 1)
dist = self.viewD dist = self.viewD
if sub is not None: if sub is not None: