From 18e9ddeb55fbe913c0eccafeae441ba75937c726 Mon Sep 17 00:00:00 2001 From: GreenWizard Date: Sat, 19 Dec 2020 18:56:41 +0200 Subject: [PATCH] fix bugs and make it work :) fat model, but its avg. okay --- CMazeExperience.py | 8 ------ Core/CMazeEnviroment.py | 7 +++-- model.py | 42 +++++++++++++---------------- train.py | 49 +++++++++++++++++---------------- view_maze.py | 58 +++++++++++++++++++++++++++++----------- weights/best.h5 | Bin 0 -> 6130536 bytes weights/latest.h5 | Bin 0 -> 6130536 bytes 7 files changed, 87 insertions(+), 77 deletions(-) create mode 100644 weights/best.h5 create mode 100644 weights/latest.h5 diff --git a/CMazeExperience.py b/CMazeExperience.py index e491447..5083588 100644 --- a/CMazeExperience.py +++ b/CMazeExperience.py @@ -13,13 +13,6 @@ def __init__(self, maxSize): def addEpisode(self, replay): score = sum(x[2] for x in replay) if score < self.minScore: return - -# for i in range(len(replay)): -# state, act, score, nextState = replay[i] -# gamma = self.gamma -# for j in range(i + 1, len(replay)): -# score += gamma * replay[j][2] -# gamma *= self.gamma self.episodes.append((replay, score)) if self.sizeLimit < len(self.episodes): @@ -57,7 +50,6 @@ def take_batch(self, batch_size): nextStateWeight = 1 if ind < len(episode) - 1 else 0 batch.append((state, act, score, nextState, nextStateWeight)) - return ( np.array([x[0] for x in batch]), np.array([x[1] for x in batch]), diff --git a/Core/CMazeEnviroment.py b/Core/CMazeEnviroment.py index e63f575..16a8636 100644 --- a/Core/CMazeEnviroment.py +++ b/Core/CMazeEnviroment.py @@ -77,9 +77,8 @@ def _takeShot(self): return (data[x - d:x + d + 1, y - d:y + d + 1] for data in (maze, fog, moves)) def minimap(self): - #maze, fog, moves = self._takeShot() - maze, fog, moves = self.maze, self.fog, self.moves - return (maze * fog, moves) + maze, fog, moves = self._takeShot() + return ((maze * fog) - (1 - fog), moves) @property def state(self): @@ -120,7 +119,7 @@ def invalidActions(self): def state2input(self): maze, moves = self.minimap() - state = np.dstack((maze, )) + state = np.dstack((maze, moves)) return state @property diff --git a/model.py b/model.py index 3c30285..d94c1b2 100644 --- a/model.py +++ b/model.py @@ -1,5 +1,6 @@ import tensorflow.keras as keras import tensorflow.keras.layers as layers +import tensorflow as tf def convBlock(prev, sz, filters): conv_1 = layers.Convolution2D(filters, (sz, sz), padding="same", activation="relu")(prev) @@ -14,28 +15,21 @@ def createModel(shape): res = convBlock(res, 3, filters=32) res = layers.Flatten()(res) - - res = layers.Dense(16 ** 2, activation='relu')(res) - res = layers.Dropout(.2)(res) - res = layers.Dense(16 ** 2, activation='relu')(res) - res = layers.Dropout(.2)(res) - res = layers.Dense(16 ** 2, activation='relu')(res) - res = layers.Dropout(.2)(res) - res = layers.Dense(8 ** 2, activation='relu')(res) - res = layers.Dropout(.2)(res) - res = layers.Dense(8 ** 2, activation='relu')(res) - res = layers.Dropout(.2)(res) - res = layers.Dense(8 ** 2, activation='relu')(res) - res = layers.Dropout(.2)(res) - res = layers.Dense(4 ** 2, activation='relu')(res) - res = layers.Dropout(.2)(res) - res = layers.Dense(4 ** 2, activation='relu')(res) - res = layers.Dropout(.2)(res) - res = layers.Dense(4 ** 2, activation='relu')(res) - res = layers.Dropout(.2)(res) - res = layers.Dense(4, activation='linear')(res) - return keras.Model( - inputs=inputs, - outputs=res - ) \ No newline at end of file + # dueling dqn + valueBranch = layers.Dense(32, activation='relu')(res) + valueBranch = layers.Dense(32, activation='relu')(valueBranch) + valueBranch = layers.Dense(32, activation='relu')(valueBranch) + valueBranch = layers.Dense(1, activation='linear')(valueBranch) + + actionsBranch = layers.Dense(128, activation='relu')(res) + actionsBranch = layers.Dense(64, activation='relu')(actionsBranch) + actionsBranch = layers.Dense(64, activation='relu')(actionsBranch) + actionsBranch = layers.Dense(64, activation='relu')(actionsBranch) + actionsBranch = layers.Dense(4, activation='linear')(actionsBranch) + + res = layers.Lambda( + lambda x: x[1] + (x[0] - tf.reduce_mean(x[0], axis=-1, keepdims=True)) + )([actionsBranch, valueBranch]) + + return keras.Model(inputs=inputs, outputs=res) \ No newline at end of file diff --git a/train.py b/train.py index 2d44ecf..86aa7f8 100644 --- a/train.py +++ b/train.py @@ -39,52 +39,51 @@ def emulate(env, model, exploreRate, exploreDecay, steps, stopOnInvalid=False): probe = model.predict(np.array([state]))[0] if not stopOnInvalid: for i in env.invalidActions(): - probe[i] = -1 + probe[i] = -float('inf') act = np.argmax(probe) if stopOnInvalid and not (act in valid): - episodeReplay.append([state, act, -1, env.state2input()]) + episodeReplay.append([state, act, -10, env.state2input()]) break prevScore = env.score env.apply(MAZE_ACTIONS[act]) - normedScore = 1 if 0 < (env.score - prevScore) else -.1 + normedScore = 1 if 0 < (env.score - prevScore) else -0.1 episodeReplay.append([state, act, normedScore, env.state2input()]) done = env.done - exploreRate = max((.01, exploreRate * exploreDecay)) + exploreRate = max((.001, exploreRate * exploreDecay)) return episodeReplay if __name__ == "__main__": - sz = 32 + sz = 64 env = CMazeEnviroment( maze=(0.8 < np.random.rand(sz, sz)).astype(np.float32), pos=(0, 0), FOV=3, minimapSize=8 ) - memory = CMazeExperience(maxSize=100) + memory = CMazeExperience(maxSize=1000) done = False - batch_size = 64 - playSteps = 64 + batch_size = 256 + playSteps = 96 - bestModelScore = 0 + bestModelScore = -float('inf') model = createModel(shape=env.input_size) model.compile( optimizer=Adam(lr=1e-3), loss='mean_squared_error' ) -# model.load_weights('model.h5') + #model.load_weights('weights/best.h5') targetModel = createModel(shape=env.input_size) - np.set_printoptions(precision=3) # collect data - while len(memory) < 50: + while len(memory) < 100: env.respawn() episodeReplay = emulate( env, model, - exploreRate=0.9, - exploreDecay=0.9, + exploreRate=1, + exploreDecay=1, steps=playSteps, stopOnInvalid=False ) @@ -92,13 +91,12 @@ def emulate(env, model, exploreRate, exploreDecay, steps, stopOnInvalid=False): if 1 < len(episodeReplay): memory.addEpisode(episodeReplay) print(len(memory), env.score) - memory.update() - train_episodes = 500 - test_episodes = 10 - exploreRate = 1 - exploreDecayPerEpoch = .9 - exploreDecay = .9 + train_episodes = 100 + test_episodes = 20 + exploreRate = .5 + exploreDecayPerEpoch = .95 + exploreDecay = .95 for epoch in range(5000): print('Epoch %d' % epoch) # train @@ -106,16 +104,17 @@ def emulate(env, model, exploreRate, exploreDecay, steps, stopOnInvalid=False): lossSum = 0 for n in range(train_episodes): states, actions, rewards, nextStates, nextReward = memory.take_batch(batch_size) - targets = targetModel.predict(nextStates) - targets[np.arange(len(targets)), actions] = rewards + np.max(targets, axis=1) * .9 * nextReward + nextScores = targetModel.predict(nextStates) + targets = targetModel.predict(states) + targets[np.arange(len(targets)), actions] = rewards + np.max(nextScores, axis=1) * .95 * nextReward lossSum += model.fit( states, targets, epochs=1, verbose=0 ).history['loss'][0] + print('Avg. train loss: %.4f' % (lossSum / train_episodes)) - print(targets[0]) # test print('Epoch %d testing' % epoch) @@ -141,6 +140,6 @@ def emulate(env, model, exploreRate, exploreDecay, steps, stopOnInvalid=False): if bestModelScore < scoreSum: bestModelScore = scoreSum print('save best model') - model.save_weights('model.h5') - model.save_weights('latest.h5') + model.save_weights('weights/best.h5') + model.save_weights('weights/latest.h5') exploreRate *= exploreDecayPerEpoch \ No newline at end of file diff --git a/view_maze.py b/view_maze.py index 10c6c61..2fd8fa1 100644 --- a/view_maze.py +++ b/view_maze.py @@ -1,18 +1,13 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- -import sys -import os import tensorflow as tf +import os -if 'COLAB_GPU' in os.environ: - # fix resolve modules - from os.path import dirname - sys.path.append(dirname(dirname(dirname(__file__)))) -else: # local GPU - gpus = tf.config.experimental.list_physical_devices('GPU') - tf.config.experimental.set_virtual_device_configuration( - gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1 * 1024)] - ) +# limit GPU usage +gpus = tf.config.experimental.list_physical_devices('GPU') +tf.config.experimental.set_virtual_device_configuration( + gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1 * 1024)] +) from Core.CMazeEnviroment import CMazeEnviroment, MazeActions import numpy as np @@ -44,6 +39,7 @@ class Colors: class App: MODES = ['manual', 'random', 'agent'] + NETWORKS = ['best', 'latest'] def __init__(self): self._running = True @@ -52,6 +48,8 @@ def __init__(self): self._mode = 'manual' self._paused = True self._speed = 20 + self._usedNetwork = self.NETWORKS[0] + return def _createMaze(self): self._maze = createMaze() @@ -65,7 +63,18 @@ def on_init(self): pygame.display.set_caption('Deep maze') self._font = pygame.font.Font(pygame.font.get_default_font(), 16) self._running = True - + + def _createNewAgent(self): + filename = 'weights/%s.h5' % self._usedNetwork + if not os.path.exists(filename): + self._usedNetwork = self.NETWORKS[0] + filename = 'weights/%s.h5' % self._usedNetwork + + self._agent = createModel(shape=self._maze.input_size) + self._agent.load_weights(filename) + self._paused = True + return + def on_event(self, event): if event.type == G.QUIT: self._running = False @@ -77,12 +86,21 @@ def on_event(self, event): self._paused = True if 'agent' == self._mode: - self._agent = createModel(shape=self._maze.input_size) - self._agent.load_weights('model.h5') + self._createNewAgent() if G.K_SPACE == event.key: self._paused = not self._paused + if 'agent' == self._mode: + if G.K_r == event.key: + self._createMaze() + if G.K_n == event.key: + self._createNewAgent() + if G.K_t == event.key: + network = next((i for i, x in enumerate(self.NETWORKS) if x == self._usedNetwork)) + self._usedNetwork = self.NETWORKS[(network + 1) % len(self.NETWORKS)] + self._createNewAgent() + if G.K_ESCAPE == event.key: self._running = False @@ -121,7 +139,7 @@ def on_loop(self): if 'agent' == self._mode: probe = self._agent.predict(np.array([self._maze.state2input()]))[0] for i in self._maze.invalidActions(): - probe[i] = -1 + probe[i] = -float('inf') pred = np.argmax(probe) act = list(MazeActions)[pred] @@ -196,12 +214,20 @@ def _renderInfo(self): False, Colors.BLUE ), (655, 35) ) + + if 'agent' == self._mode: + self._display_surf.blit( + self._font.render( + 'Network: %s' % (self._usedNetwork), + False, Colors.BLUE + ), (655, 55) + ) return def on_render(self): self._display_surf.fill(Colors.SILVER) self._renderMaze() -# self._renderMazeMinimap() + self._renderMazeMinimap() self._renderInfo() pygame.display.flip() diff --git a/weights/best.h5 b/weights/best.h5 new file 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