reworks loss function and training data, cleans up directions in code
This commit is contained in:
zomseffen
parent
cf4d773c10
commit
26e7ffb12b
2 changed files with 129 additions and 175 deletions
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@ -1,7 +1,7 @@
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import torch
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from torch import nn
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import numpy as np
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import tqdm
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from tqdm import tqdm
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from torch.utils.data import Dataset, DataLoader
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import os
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@ -14,6 +14,7 @@ print(f"Using {device} device")
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# Define model
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class BaseModel(nn.Module):
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evolutionary = False
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def __init__(self, view_dimension, action_num, channels):
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super(BaseModel, self).__init__()
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self.flatten = nn.Flatten()
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@ -59,11 +60,18 @@ def create_optimizer(model):
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def create_loss_function(action):
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lambda_factor = 0.0
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split_factor = 1.0
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def custom_loss(prediction, target):
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return torch.mean(0.5 * torch.square(
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0.1 * target[:, 0, 0] + target[:, 1, 0] - (
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prediction[:, action, 0] + prediction[:, action, 1])) + 0.5 * torch.square(
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target[:, 1, 0] - prediction[:, action, 0]), dim=0)
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return torch.mean(split_factor * torch.square(
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# discounted best estimate the old weights made for t+1
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lambda_factor * target[:, 0, 0] +
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# actual reward for t
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target[:, 1, 0] -
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# estimate for current weights
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(prediction[:, action, 0] + prediction[:, action, 1])) +
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# trying to learn present reward separate from future reward
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(1.0 - split_factor) * torch.square(target[:, 1, 0] - prediction[:, action, 0]), dim=0)
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return custom_loss
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@ -75,26 +83,31 @@ def from_numpy(x):
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def train(states, targets, model, optimizer):
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for action in range(model.action_num):
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data_set = BaseDataSet(states[action], targets[action])
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dataloader = DataLoader(data_set, batch_size=64, shuffle=True)
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dataloader = DataLoader(data_set, batch_size=256, shuffle=True)
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loss_fn = create_loss_function(action)
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size = len(dataloader)
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model.train()
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for batch, (X, y) in enumerate(dataloader):
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X, y = X.to(device), y.to(device)
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# Compute prediction error
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pred = model(X)
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loss = loss_fn(pred, y)
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epochs = 1
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with tqdm(range(epochs)) as progress_bar:
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for _ in enumerate(progress_bar):
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losses = []
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for batch, (X, y) in enumerate(dataloader):
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X, y = X.to(device), y.to(device)
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# Backpropagation
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optimizer.zero_grad()
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loss.backward(retain_graph=True)
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optimizer.step()
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# Compute prediction error
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pred = model(X)
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loss = loss_fn(pred, y)
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if batch % 100 == 0:
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loss, current = loss.item(), batch * len(X)
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print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]")
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# Backpropagation
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optimizer.zero_grad()
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loss.backward(retain_graph=True)
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optimizer.step()
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losses.append(loss.item())
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progress_bar.set_postfix(loss=np.average(losses))
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progress_bar.update()
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model.eval()
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del data_set
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@ -108,34 +108,6 @@ class QLearner(Subject):
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def createState(self, world: LabyrinthWorld):
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state = np.zeros((2 * self.viewD + 1, 2 * self.viewD + 1), np.int) # - 1
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# # floodfill state
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# queued = [(0, 0)]
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# todo = [(0, 0, 0)]
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# while todo != []:
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# doing = todo.pop(0)
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#
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# if self.x + doing[0] >= 0 and self.x + doing[0] < 64 and self.y + doing[1] >= 0 and self.y + doing[1] < 64:
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# value = world.board[self.x + doing[0], self.y + doing[1]]
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# state[self.viewD + doing[0], self.viewD + doing[1]] = value
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#
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# # if value == 3:
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# # state[self.viewD + doing[0], self.viewD + doing[1]] = value
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#
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# if value != 0 and doing[2] < self.viewD:
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# for i in range(-1, 2, 1):
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# for j in range(-1, 2, 1):
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# # 4-neighbour. without it it is 8-neighbour
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# if abs(i) + abs(j) == 1:
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# if (doing[0] + i, doing[1] + j) not in queued:
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# queued.append((doing[0] + i, doing[1] + j))
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# todo.append((doing[0] + i, doing[1] + j, doing[2] + 1))
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#
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# for sub in world.subjects:
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# if (sub.x - self.x, sub.y - self.y) in queued and state[
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# self.viewD + sub.x - self.x, self.viewD + sub.y - self.y] != 3:
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# state[self.viewD + sub.x - self.x, self.viewD + sub.y - self.y] = state[
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# self.viewD + sub.x - self.x, self.viewD + sub.y - self.y] * 100 + sub.col
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maxdirleft = self.x - max(self.x - (self.viewD), 0)
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maxdirright = min(self.x + (self.viewD), (world.board_shape[0] - 1)) - self.x
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maxdirup = self.y - max(self.y - (self.viewD), 0)
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@ -300,6 +272,7 @@ class DoubleQLearner(QLearner):
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pass
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RECALCULATE = False
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class NetLearner(Subject):
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right = (1, 0)
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left = (-1, 0)
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@ -440,7 +413,6 @@ class NetLearner(Subject):
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axs[1, 1].set_title('grass')
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plt.show(block=True)
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def createState(self, world: LabyrinthWorld):
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state = np.zeros((2 * self.viewD + 1, 2 * self.viewD + 1), np.float) # - 1
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state2 = np.zeros((2 * self.viewD + 1, 2 * self.viewD + 1), np.float) # - 1
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@ -474,10 +446,8 @@ class NetLearner(Subject):
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action = self.lastAction
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return np.reshape(np.concatenate((area, action)), (1, 4 * (2 * self.viewD + 1) * (2 * self.viewD + 1) + 2))
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def calculateAction(self, world: LabyrinthWorld, vals=None, state=None):
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# 0, 0 is top left
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def generate_valid_directions(self, world: LabyrinthWorld):
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directions = []
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if self.x - 1 >= 0:
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if world.board[self.x - 1, self.y] != 0:
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directions.append(self.left)
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@ -493,9 +463,15 @@ class NetLearner(Subject):
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if self.y + 1 < world.board_shape[1]:
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if world.board[self.x, self.y + 1] != 0:
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directions.append(self.down)
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return directions
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def calculateAction(self, world: LabyrinthWorld, vals=None, state=None):
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# 0, 0 is top left
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directions = self.generate_valid_directions(world)
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if directions == []:
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print('Wut?')
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return
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if directions != [] and self.alive:
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if state is None:
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@ -550,7 +526,8 @@ class NetLearner(Subject):
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self.nextTrain = min(self.batchsize + self.nextTrain, (self.historySizeMul + 1) * self.batchsize)
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print(len(self.samples), self.nextTrain)
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self.accumulated_rewards += self.lastReward
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if not self.random:
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self.accumulated_rewards += self.lastReward
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self.lastAction = self.action
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self.lastState = self.state
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@ -562,27 +539,10 @@ class NetLearner(Subject):
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self.executeAction(world, self.action)
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def randomAct(self, world: LabyrinthWorld):
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right = (1, 0)
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left = (-1, 0)
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up = (0, -1)
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down = (0, 1)
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directions = []
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directions = self.generate_valid_directions(world)
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if self.x - 1 >= 0:
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if world.board[self.x - 1, self.y] != 0:
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directions.append(left)
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if self.x + 1 < world.board_shape[0]:
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if world.board[self.x + 1, self.y] != 0:
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directions.append(right)
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if self.y - 1 >= 0:
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if world.board[self.x, self.y - 1] != 0:
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directions.append(up)
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if self.y + 1 < world.board_shape[1]:
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if world.board[self.x, self.y + 1] != 0:
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directions.append(down)
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if len(directions) == 0:
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return 0, 0
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d = random.randint(0, len(directions) - 1)
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action = directions[d]
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@ -616,16 +576,16 @@ class NetLearner(Subject):
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replace=True)
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samples = samples[index]
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# self.samples = []
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target[:, 1, 0] = samples[:, 0, 3] # reward t-2 got
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if partTwo:
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target[:, 1, 0] = samples[:, 1, 3] #reward t-2 got
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if RECALCULATE:
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nextState = np.concatenate(samples[:, 1, 0]) #states of t-1
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nextVals = self.model(from_numpy(nextState)).detach().numpy()
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nextState = np.concatenate(samples[:, 1, 0]) #states of t-1
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nextVals = self.model(from_numpy(nextState)).detach().numpy()
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nextVals2 = nextVals[:, i, 0] + nextVals[:, i, 1]
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target[:, 0, 0] = nextVals2 #best q t-1
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else:
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target[:, 1, 0] = np.array(list(map(lambda elem: list(elem), list(np.array(samples[:, 1, 4])))))[:, i] # reward t-2 got
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nextVals2 = np.max(nextVals[:, :, 0] + nextVals[:, :, 1], axis=1)
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target[:, 0, 0] = nextVals2 #best q t-1
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else:
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target[:, 0, 0] = samples[:, 1, 2] #best q t-1
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targets.append(target)
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@ -697,27 +657,7 @@ class Herbivore(NetLearner):
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return np.reshape(np.concatenate((area, action)), (1, 4 * (2 * self.viewD + 1) * (2 * self.viewD + 1) + 2))
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def executeAction(self, world: LabyrinthWorld, action):
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right = (1, 0)
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left = (-1, 0)
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up = (0, -1)
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down = (0, 1)
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directions = []
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if self.x - 1 >= 0:
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if world.board[self.x - 1, self.y] != 0:
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directions.append(left)
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if self.x + 1 < world.board_shape[0]:
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if world.board[self.x + 1, self.y] != 0:
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directions.append(right)
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if self.y - 1 >= 0:
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if world.board[self.x, self.y - 1] != 0:
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directions.append(up)
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if self.y + 1 < world.board_shape[1]:
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if world.board[self.x, self.y + 1] != 0:
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directions.append(down)
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directions = self.generate_valid_directions(world)
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if len(action) == 2:
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if len(world.subjectDict[(self.x + action[0], self.y + action[1])]) > 0:
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for sub in world.subjectDict[(self.x + action[0], self.y + action[1])]:
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@ -729,26 +669,26 @@ class Herbivore(NetLearner):
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self.alive = False
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self.lastRewards = []
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if right in directions:
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if self.right in directions:
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self.lastRewards.append(world.grass[self.x + 1, self.y])
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else:
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self.lastRewards.append(0)
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if left in directions:
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if self.left in directions:
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self.lastRewards.append(world.grass[self.x - 1, self.y])
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else:
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self.lastRewards.append(0)
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if up in directions:
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if self.up in directions:
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self.lastRewards.append(world.grass[self.x, self.y - 1])
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else:
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self.lastRewards.append(0)
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if down in directions:
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if self.down in directions:
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self.lastRewards.append(world.grass[self.x, self.y + 1])
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else:
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self.lastRewards.append(0)
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assert len(self.lastRewards) == 4, 'Last Rewards not filled correctly!'
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world.subjectDict[(self.x, self.y)].remove(self)
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self.lastReward += world.trailMix[self.x, self.y]
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# self.lastReward += world.trailMix[self.x, self.y]
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self.x += action[0]
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self.y += action[1]
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world.subjectDict[(self.x, self.y)].append(self)
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@ -757,33 +697,58 @@ class Herbivore(NetLearner):
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world.grass[self.x, self.y] = 0
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world.hunter_grass[self.x, self.y] = 0
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def generate_valid_directions(self, world: LabyrinthWorld):
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directions = []
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if self.x - 1 >= 0:
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if world.board[self.x - 1, self.y] != 0:
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if not world.subjectDict[(self.x - 1, self.y)]:
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directions.append(self.left)
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if self.x + 1 < world.board_shape[0]:
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if world.board[self.x + 1, self.y] != 0:
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if not world.subjectDict[(self.x + 1, self.y)]:
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directions.append(self.right)
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if self.y - 1 >= 0:
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if world.board[self.x, self.y - 1] != 0:
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if not world.subjectDict[(self.x, self.y - 1)]:
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directions.append(self.up)
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if self.y + 1 < world.board_shape[1]:
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if world.board[self.x, self.y + 1] != 0:
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if not world.subjectDict[(self.x, self.y + 1)]:
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directions.append(self.down)
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return directions
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def randomAct(self, world: LabyrinthWorld):
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right = (1, 0)
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left = (-1, 0)
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up = (0, -1)
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down = (0, 1)
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directions = []
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actDict = {}
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if self.x - 1 >= 0:
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if world.board[self.x - 1, self.y] != 0:
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directions.append(left)
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actDict[left] = world.grass[self.x - 1, self.y]
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if not world.subjectDict[(self.x - 1, self.y)]:
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directions.append(self.left)
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actDict[self.left] = world.grass[self.x - 1, self.y]
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if self.x + 1 < world.board_shape[0]:
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if world.board[self.x + 1, self.y] != 0:
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directions.append(right)
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actDict[right] = world.grass[self.x + 1, self.y]
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if not world.subjectDict[(self.x + 1, self.y)]:
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directions.append(self.right)
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actDict[self.right] = world.grass[self.x + 1, self.y]
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if self.y - 1 >= 0:
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if world.board[self.x, self.y - 1] != 0:
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directions.append(up)
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actDict[up] = world.grass[self.x, self.y - 1]
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if not world.subjectDict[(self.x, self.y - 1)]:
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directions.append(self.up)
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actDict[self.up] = world.grass[self.x, self.y - 1]
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if self.y + 1 < world.board_shape[1]:
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if world.board[self.x, self.y + 1] != 0:
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directions.append(down)
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actDict[down] = world.grass[self.x, self.y + 1]
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if not world.subjectDict[(self.x, self.y + 1)]:
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directions.append(self.down)
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actDict[self.down] = world.grass[self.x, self.y + 1]
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if len(directions) == 0:
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return 0, 0
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allowedActions = dict(filter(lambda elem: elem[0] in directions, actDict.items()))
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action = max(allowedActions, key=allowedActions.get)
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@ -792,7 +757,7 @@ class Herbivore(NetLearner):
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def respawnUpdate(self, x, y, world: LabyrinthWorld):
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super(Herbivore, self).respawnUpdate(x, y, world)
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self.lastReward -= 1
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# self.lastReward -= 1
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class Hunter(NetLearner):
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@ -802,16 +767,12 @@ class Hunter(NetLearner):
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g = 255
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b = 255
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def randomAct(self, world: LabyrinthWorld):
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right = (1, 0)
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left = (-1, 0)
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up = (0, -1)
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down = (0, 1)
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directions = []
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actDict = {}
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if self.x - 1 >= 0:
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if world.board[self.x - 1, self.y] > 0.01:
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directions.append(left)
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directions.append(self.left)
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sub = self.getClosestSubject(world, self.x - 1, self.y)
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dist = self.viewD
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@ -819,15 +780,15 @@ class Hunter(NetLearner):
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dist = np.sqrt(np.square(self.x - 1 - sub.x) + np.square(self.y - sub.y))
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distReward = self.viewD - dist
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actDict[left] = world.trailMix[self.x - 1, self.y] + world.hunter_grass[self.x - 1, self.y] * self.hunterGrassScale + distReward
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if len(world.subjectDict[(self.x + left[0], self.y + left[1])]) > 0:
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for sub in world.subjectDict[(self.x + left[0], self.y + left[1])]:
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actDict[self.left] = world.trailMix[self.x - 1, self.y] + world.hunter_grass[self.x - 1, self.y] * self.hunterGrassScale + distReward
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if len(world.subjectDict[(self.x + self.left[0], self.y + self.left[1])]) > 0:
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for sub in world.subjectDict[(self.x + self.left[0], self.y + self.left[1])]:
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if sub.col != self.col:
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actDict[left] += 10
|
||||
actDict[self.left] += 10
|
||||
|
||||
if self.x + 1 < world.board_shape[0]:
|
||||
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)
|
||||
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))
|
||||
distReward = self.viewD - dist
|
||||
|
||||
actDict[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:
|
||||
for sub in world.subjectDict[(self.x + right[0], self.y + right[1])]:
|
||||
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 + self.right[0], self.y + self.right[1])]) > 0:
|
||||
for sub in world.subjectDict[(self.x + self.right[0], self.y + self.right[1])]:
|
||||
if sub.col != self.col:
|
||||
actDict[right] += 10
|
||||
actDict[self.right] += 10
|
||||
|
||||
if self.y - 1 >= 0:
|
||||
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)
|
||||
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))
|
||||
distReward = self.viewD - dist
|
||||
|
||||
actDict[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:
|
||||
for sub in world.subjectDict[(self.x + up[0], self.y + up[1])]:
|
||||
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 + self.up[0], self.y + self.up[1])]) > 0:
|
||||
for sub in world.subjectDict[(self.x + self.up[0], self.y + self.up[1])]:
|
||||
if sub.col != self.col:
|
||||
actDict[up] += 10
|
||||
actDict[self.up] += 10
|
||||
|
||||
if self.y + 1 < world.board_shape[1]:
|
||||
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)
|
||||
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))
|
||||
distReward = self.viewD - dist
|
||||
|
||||
actDict[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:
|
||||
for sub in world.subjectDict[(self.x + down[0], self.y + down[1])]:
|
||||
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 + self.down[0], self.y + self.down[1])]) > 0:
|
||||
for sub in world.subjectDict[(self.x + self.down[0], self.y + self.down[1])]:
|
||||
if sub.col != self.col:
|
||||
actDict[down] += 10
|
||||
actDict[self.down] += 10
|
||||
|
||||
if len(actDict) > 0:
|
||||
allowedActions = dict(filter(lambda elem: elem[0] in directions, actDict.items()))
|
||||
|
|
@ -919,47 +880,27 @@ class Hunter(NetLearner):
|
|||
def executeAction(self, world: LabyrinthWorld, action):
|
||||
grass_factor = 0.5
|
||||
|
||||
right = (1, 0)
|
||||
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)
|
||||
directions = self.generate_valid_directions(world)
|
||||
|
||||
if len(action) == 2:
|
||||
right_kill = left_kill = up_kill = down_kill = False
|
||||
if right in directions:
|
||||
for sub in world.subjectDict[(self.x + action[0], self.y + action[1])]:
|
||||
if self.right in directions:
|
||||
for sub in world.subjectDict[(self.x + self.right[0], self.y + self.right[1])]:
|
||||
if sub.alive:
|
||||
if sub.col != self.col:
|
||||
right_kill = True
|
||||
if left in directions:
|
||||
for sub in world.subjectDict[(self.x + left[0], self.y + left[1])]:
|
||||
if self.left in directions:
|
||||
for sub in world.subjectDict[(self.x + self.left[0], self.y + self.left[1])]:
|
||||
if sub.alive:
|
||||
if sub.col != self.col:
|
||||
left_kill = True
|
||||
if up in directions:
|
||||
for sub in world.subjectDict[(self.x + up[0], self.y + up[1])]:
|
||||
if self.up in directions:
|
||||
for sub in world.subjectDict[(self.x + self.up[0], self.y + self.up[1])]:
|
||||
if sub.alive:
|
||||
if sub.col != self.col:
|
||||
up_kill = True
|
||||
if down in directions:
|
||||
for sub in world.subjectDict[(self.x + down[0], self.y + down[1])]:
|
||||
if self.down in directions:
|
||||
for sub in world.subjectDict[(self.x + self.down[0], self.y + self.down[1])]:
|
||||
if sub.alive:
|
||||
if sub.col != self.col:
|
||||
down_kill = True
|
||||
|
|
@ -974,7 +915,7 @@ class Hunter(NetLearner):
|
|||
self.alive = True
|
||||
|
||||
self.lastRewards = []
|
||||
if right in directions:
|
||||
if self.right in directions:
|
||||
sub = self.getClosestSubject(world, self.x + 1, self.y)
|
||||
dist = self.viewD
|
||||
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)
|
||||
else:
|
||||
self.lastRewards.append(0)
|
||||
if left in directions:
|
||||
if self.left in directions:
|
||||
sub = self.getClosestSubject(world, self.x - 1, self.y)
|
||||
dist = self.viewD
|
||||
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)
|
||||
else:
|
||||
self.lastRewards.append(0)
|
||||
if up in directions:
|
||||
if self.up in directions:
|
||||
sub = self.getClosestSubject(world, self.x, self.y - 1)
|
||||
dist = self.viewD
|
||||
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)
|
||||
else:
|
||||
self.lastRewards.append(0)
|
||||
if down in directions:
|
||||
if self.down in directions:
|
||||
sub = self.getClosestSubject(world, self.x, self.y + 1)
|
||||
dist = self.viewD
|
||||
if sub is not None:
|
||||
|
|
|
|||
Loading…
Reference in a new issue