move to pytorch

labirinth_ai/LabyrinthClient.py

@@ -17,9 +17,9 @@ class LabyrinthClient(Client):
                 if self.world_provider.world.board[x, y] in [1, 2]:
                     r, g, b = 57, 92, 152
                     if 1.5 >= self.world_provider.world.hunter_grass[x, y] > 0.5:
-                        r, g, b = 25, 149, 156
-                    if 3 >= self.world_provider.world.hunter_grass[x, y] > 1.5:
                         r, g, b = 112, 198, 169
+                    if 3 >= self.world_provider.world.hunter_grass[x, y] > 1.5:
+                        r, g, b = 25, 149, 156
                     self.world_provider.world.set_color(x, y, 0, r / 255.0, g / 255.0, b / 255.0)
                 if self.world_provider.world.board[x, y] == 3:
                     self.world_provider.world.set_color(x, y, 0, 139 / 255.0, 72 / 255.0, 82 / 255.0)

labirinth_ai/Models/BaseModel.py

@@ -0,0 +1,125 @@
+import torch
+from torch import nn
+import numpy as np
+import tqdm
+from torch.utils.data import Dataset, DataLoader
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Using {device} device")
+
+
+# Define model
+class BaseModel(nn.Module):
+    def __init__(self, view_dimension, action_num, channels):
+        super(BaseModel, self).__init__()
+        self.flatten = nn.Flatten()
+        self.actions = []
+        self.action_num = action_num
+        self.viewD = view_dimension
+        self.channels = channels
+        for action in range(action_num):
+            action_sequence = nn.Sequential(
+                nn.Linear(channels * (2 * self.viewD + 1) * (2 * self.viewD + 1) + 2,
+                          (2 * self.viewD + 1) * (2 * self.viewD + 1)),
+                nn.ELU(),
+                nn.Linear((2 * self.viewD + 1) * (2 * self.viewD + 1), (self.viewD + 1) * (self.viewD + 1)),
+                nn.ELU(),
+                nn.Linear((self.viewD + 1) * (self.viewD + 1), 2)
+            )
+            self.add_module('action_' + str(action), action_sequence)
+            self.actions.append(action_sequence)
+
+    def forward(self, x):
+        x_flat = self.flatten(x)
+        actions = []
+        for action in range(self.action_num):
+            actions.append(self.actions[action](x_flat))
+        return torch.stack(actions, dim=1)
+
+
+class BaseDataSet(Dataset):
+    def __init__(self, states, targets):
+        assert len(states) == len(targets), "Needs to have as many states as targets!"
+        self.states = torch.tensor(states, dtype=torch.float32)
+        self.targets = torch.tensor(targets, dtype=torch.float32)
+
+    def __len__(self):
+        return len(self.states)
+
+    def __getitem__(self, idx):
+        return self.states[idx], self.targets[idx]
+
+
+def create_optimizer(model):
+    return torch.optim.RMSprop(model.parameters(), lr=1e-3)
+
+
+def create_loss_function(action):
+    def custom_loss(prediction, target):
+        return torch.mean(0.5 * torch.square(
+            0.1 * target[:, 0, 0] + target[:, 1, 0] - (
+                        prediction[:, action, 0] + prediction[:, action, 1])) + 0.5 * torch.square(
+            target[:, 1, 0] - prediction[:, action, 0]), dim=0)
+
+    return custom_loss
+
+
+def from_numpy(x):
+    return torch.tensor(x, dtype=torch.float32)
+
+
+def train(states, targets, model, optimizer):
+    for action in range(model.action_num):
+        data_set = BaseDataSet(states[action], targets[action])
+        dataloader = DataLoader(data_set, batch_size=64, shuffle=True)
+        loss_fn = create_loss_function(action)
+
+        size = len(dataloader)
+        model.train()
+        for batch, (X, y) in enumerate(dataloader):
+            X, y = X.to(device), y.to(device)
+
+            # Compute prediction error
+            pred = model(X)
+            loss = loss_fn(pred, y)
+
+            # Backpropagation
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+            if batch % 100 == 0:
+                loss, current = loss.item(), batch * len(X)
+                print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")
+        model.eval()
+
+
+if __name__ == '__main__':
+    sample = np.random.random((1, 4, 11, 11))
+
+    model = BaseModel(5, 4, 4).to(device)
+    print(model)
+
+    test = model(torch.tensor(sample, dtype=torch.float32))
+    # test = test.cpu().detach().numpy()
+    print(test)
+
+    state = np.random.random((4, 11, 11))
+    target = np.random.random((4, 2))
+    states = [
+        [state],
+        [state],
+        [state],
+        [state],
+    ]
+    targets = [
+        [target],
+        [target],
+        [target],
+        [target],
+    ]
+
+    optimizer = torch.optim.RMSprop(model.parameters(), lr=1e-3)
+
+    train(states, targets, model, optimizer)
+

labirinth_ai/Subject.py

@@ -5,6 +5,7 @@ from tensorflow import keras
 
 from labirinth_ai.LabyrinthWorld import LabyrinthWorld
 from labirinth_ai.loss import loss2, loss3
+from labirinth_ai.Models.BaseModel import BaseModel, train, create_optimizer, device, from_numpy
 
 # import torch
 # dtype = torch.float
@@ -369,22 +370,9 @@ class NetLearner(Subject):
         self.x_in = []
         self.actions = []
         self.target = []
-        for i in range(4):
-            x_in = keras.Input(shape=(self.channels * (2 * self.viewD + 1) * (2 * self.viewD + 1) + 2))
-            self.x_in.append(x_in)
-            inVec = keras.layers.Flatten()(x_in)
-            actions = keras.layers.Dense(((2 * self.viewD + 1) * (2 * self.viewD + 1)), activation='elu',
-                                         kernel_regularizer=keras.regularizers.l2(0.001),
-                                         name=self.name + str(self.id) + 'Dense' + str(i) + 'l1')(inVec)
-            actions = keras.layers.Dense(((self.viewD + 1) * (self.viewD + 1)), activation='elu',
-                                         kernel_regularizer=keras.regularizers.l2(0.001))(actions)
-            self.target.append(keras.Input(shape=(2, 1)))
-            self.actions.append(keras.layers.Dense(2, activation='linear', use_bias=False, kernel_regularizer=keras.regularizers.l2(0.001))(actions))
-
-        self.model = keras.Model(inputs=self.x_in, outputs=self.actions)
-
-        self.model.compile(optimizer=tf.keras.optimizers.RMSprop(self.learningRate), loss=loss3,
-                           target_tensors=self.target)
+        self.model = BaseModel(self.viewD, 4, 4)
+        self.model.to(device)
+        self.optimizer = create_optimizer(self.model)
 
         if len(self.samples) < self.randomBuffer:
             self.random = True
@@ -508,7 +496,7 @@ class NetLearner(Subject):
             if state is None:
                 state = self.createState(world)
             if vals is None:
-                vals = self.model.predict([state, state, state, state])
+                vals = self.model(from_numpy(state)).detach().numpy()
                 vals = np.reshape(np.transpose(np.reshape(vals, (4, 2)), (1, 0)),
                                   (1, 8))
 
@@ -623,9 +611,9 @@ class NetLearner(Subject):
                 target[:, 1, 0] = samples[:, 1, 3] #reward t-2 got
 
                 nextState = np.concatenate(samples[:, 1, 0]) #states of t-1
-                nextVals = self.model.predict([nextState, nextState, nextState, nextState])
+                nextVals = self.model(from_numpy(nextState)).detach().numpy()
 
-                nextVals2 = nextVals[i][:,  0] + nextVals[i][:, 1]
+                nextVals2 = nextVals[:, i,  0] + nextVals[:, i, 1]
                 target[:, 0, 0] = nextVals2 #best q t-1
             else:
                 target[:, 1, 0] = np.array(list(map(lambda elem: list(elem), list(np.array(samples[:, 1, 4])))))[:, i]  # reward t-2 got
@@ -639,7 +627,7 @@ class NetLearner(Subject):
     def train(self):
         print(self.name)
         states, target = self.generateSamples()
-        self.model.fit(states, target, epochs=1)
+        train(states, target, self.model, self.optimizer)
 
         self.samples = self.samples[-self.historySizeMul*self.batchsize:]