Updated python examples, added script to test building with all gcc a…

…nd python combinations

examples/python/README.md

@@ -10,12 +10,15 @@ Demonstrates how to use the most basic features of the environment. It configure
 
 ## [buffers.py](https://github.com/mwydmuch/ViZDoom/blob/master/examples/python/buffers.py)
 
-## [cig.py](https://github.com/mwydmuch/ViZDoom/blob/master/examples/python/cig.py), [cig_host.py](https://github.com/mwydmuch/ViZDoom/blob/master/examples/python/cig_host.py)
+## [cig_multiplayer.py](https://github.com/mwydmuch/ViZDoom/blob/master/examples/python/cig_multiplayer.py), [cig_multiplayer_host.py](https://github.com/mwydmuch/ViZDoom/blob/master/examples/python/cig_multiplayer_host.py)
 Demonstrates how to configure and play multiplayer game for CIG competition.
 
-## [cig_bots.py](https://github.com/mwydmuch/ViZDoom/blob/master/examples/python/cig_bots.py)
+## [cig_multiplayer_bots.py](https://github.com/mwydmuch/ViZDoom/blob/master/examples/python/cig_multiplayer_bots.py)
 Demonstrates how to play with bots to simulate multiplayer game. Helpful for developing AI agent for CIG competition.
 
+## [cig_singleplayer.py](https://github.com/mwydmuch/ViZDoom/blob/master/examples/python/cig_singleplayer.py), [cig_multiplayer_host.py](https://github.com/mwydmuch/ViZDoom/blob/master/examples/python/cig_singleplayer_host.py)
+Demonstrates how to configure and play singleplayer game for CIG competition.
+
 ## [delta_buttons.py](https://github.com/mwydmuch/ViZDoom/blob/master/examples/python/delta_buttons.py)
 Shows how delta buttons work (they may take values other than 0 and 1 and can be used for precise movement).
 
@@ -27,7 +30,7 @@ Tests the performance of the environment in frames per second. It should give yo
 
 ## [labels.py](https://github.com/mwydmuch/ViZDoom/blob/master/examples/python/labels.py)
 
-## [learning_pytorch.py](https://github.com/mwydmuch/ViZDoom/blob/master/examples/python/learning_pytorch.py)
+## [learning_pytorch.py](https://github.com/mwydmuch/ViZDoom/blob/master/examples/python/learning_pytorch.py), [learning_pytorch_test.py](https://github.com/mwydmuch/ViZDoom/blob/master/examples/python/learning_pytorch_test.py)
 Contains an example of how to implement basic Q-learning on the interface within PyTorch
 
 ## [learning_theano.py](https://github.com/mwydmuch/ViZDoom/blob/master/examples/python/learning_theano.py)
@@ -36,7 +39,7 @@ Contains an example of how to implement basic Q-learning on the interface within
 ## [learning_tensorflow.py](https://github.com/mwydmuch/ViZDoom/blob/master/examples/python/learning_tensorflow.py)
 Contains an example of how to implement basic Q-learning on the interface within Tensorflow
 
-## [multiple_instances.py](https://github.com/mwydmuch/ViZDoom/blob/master/examples/python/multiple_instances.py) and [multiple_instances_advance.py](https://github.com/mwydmuch/ViZDoom/blob/master/examples/python/multiple_instances_advance.py)
+## [multiple_instances.py](https://github.com/mwydmuch/ViZDoom/blob/master/examples/python/multiple_instances.py), [multiple_instances_advance.py](https://github.com/mwydmuch/ViZDoom/blob/master/examples/python/multiple_instances_advance.py)
 Contains an example of how to create a "server" and have multiple agents playing on the server at once. Combine with bots.py and learning_x.py to train agents against some AI
 
 ## [oblige.py](https://github.com/mwydmuch/ViZDoom/blob/master/examples/python/multiple_instances.py) (level generator)

examples/python/learning_pytorch_test.py

@@ -0,0 +1,128 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+# E. Culurciello
+# August 2017
+
+from __future__ import division
+from __future__ import print_function
+from vizdoom import *
+import itertools as it
+from random import sample, randint, random
+from time import time, sleep
+import numpy as np
+import skimage.color, skimage.transform
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torchvision import datasets, transforms
+from torch.autograd import Variable
+
+
+# NN learning settings
+batch_size = 64
+
+# Training regime
+test_episodes_per_epoch = 100
+
+# Other parameters
+frame_repeat = 12
+resolution = (30, 45)
+episodes_to_watch = 10
+
+model_savefile = "./model-doom.pth"
+save_model = True
+load_model = False
+skip_learning = False
+# Configuration file path
+config_file_path = "../../scenarios/simpler_basic.cfg"
+
+
+# config_file_path = "../../scenarios/rocket_basic.cfg"
+# config_file_path = "../../scenarios/basic.cfg"
+
+# Converts and down-samples the input image
+def preprocess(img):
+    img = skimage.transform.resize(img, resolution)
+    img = img.astype(np.float32)
+    return img
+
+
+class Net(nn.Module):
+    def __init__(self, available_actions_count):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(1, 8, kernel_size=6, stride=3)
+        self.conv2 = nn.Conv2d(8, 8, kernel_size=3, stride=2)
+        self.fc1 = nn.Linear(192, 128)
+        self.fc2 = nn.Linear(128, available_actions_count)
+
+    def forward(self, x):
+        x = F.relu(self.conv1(x))
+        x = F.relu(self.conv2(x))
+        x = x.view(-1, 192)
+        x = F.relu(self.fc1(x))
+        return self.fc2(x)
+
+
+def get_q_values(state):
+    state = torch.from_numpy(state)
+    state = Variable(state)
+    return model(state)
+
+def get_best_action(state):
+    q = get_q_values(state)
+    m, index = torch.max(q, 1)
+    action = index.data.numpy()[0]
+    return action
+
+
+# Creates and initializes ViZDoom environment.
+def initialize_vizdoom(config_file_path):
+    print("Initializing doom...")
+    game = DoomGame()
+    game.load_config(config_file_path)
+    game.set_window_visible(True)
+    game.set_mode(Mode.PLAYER)
+    game.set_screen_format(ScreenFormat.GRAY8)
+    game.set_screen_resolution(ScreenResolution.RES_640X480)
+    game.init()
+    print("Doom initialized.")
+    return game
+
+
+if __name__ == '__main__':
+    # Create Doom instance
+    game = initialize_vizdoom(config_file_path)
+
+    # Action = which buttons are pressed
+    n = game.get_available_buttons_size()
+    actions = [list(a) for a in it.product([0, 1], repeat=n)]
+
+    print("Loading model from: ", model_savefile)
+    model = torch.load(model_savefile)
+
+    print("======================================")
+    print("Testing trained neural network!")
+
+    # Reinitialize the game with window visible
+    game.set_window_visible(True)
+    game.set_mode(Mode.ASYNC_PLAYER)
+    game.init()
+
+    for _ in range(episodes_to_watch):
+        game.new_episode()
+        while not game.is_episode_finished():
+            state = preprocess(game.get_state().screen_buffer)
+            state = state.reshape([1, 1, resolution[0], resolution[1]])
+            best_action_index = get_best_action(state)
+
+            # Instead of make_action(a, frame_repeat) in order to make the animation smooth
+            game.set_action(actions[best_action_index])
+            for _ in range(frame_repeat):
+                game.advance_action()
+                sleep(0.03)
+
+        # Sleep between episodes
+        sleep(1.0)
+        score = game.get_total_reward()
+        print("Total score: ", score)