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Ronnasayd/cartpole_dqn_play.py

Created October 13, 2020 20:50
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Algoritmos e implementações para diversas redes neurais
Raw
cartpole_dqn_play.py
import numpy as np
import gym
from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten
from keras.optimizers import Adam
from rl.agents.dqn import DQNAgent
from rl.policy import EpsGreedyQPolicy
from rl.memory import SequentialMemory
ENV_NAME = 'CartPole-v0'
# Get the environment and extract the number of actions.
env = gym.make(ENV_NAME)
np.random.seed(123)
env.seed(123)
nb_actions = env.action_space.n
# Next, we build a very simple model.
model = Sequential()
model.add(Flatten(input_shape=(1, ) + env.observation_space.shape))
model.add(Dense(16))
model.add(Activation('relu'))
model.add(Dense(16))
model.add(Activation('relu'))
model.add(Dense(16))
model.add(Activation('relu'))
model.add(Dense(nb_actions))
model.add(Activation('linear'))
print(model.summary())
# Finally, we configure and compile our agent. You can use every built-in Keras optimizer and
# even the metrics!
memory = SequentialMemory(limit=50000, window_length=1)
policy = EpsGreedyQPolicy()
dqn = DQNAgent(model=model,
nb_actions=nb_actions,
memory=memory,
nb_steps_warmup=10,
target_model_update=0.01,
policy=policy)
dqn.compile(Adam(lr=1e-3), metrics=['mae'])
dqn.load_weights('dqn_CartPole-v0_weights.h5f')
dqn.test(env, nb_episodes=15, visualize=True, verbose=2)
Raw
frozen2_q_learning.py
import gym
import time
import numpy as np
import random
env = gym.make('FrozenLake8x8-v0') # selecionar o ambiente
Q = np.ones(
(env.observation_space.n, env.action_space.n), dtype=np.float32
) # construir a tabela Q onde as linhas sao os estados possiveis e as colunas as ações
epsilon = 1
epsilon_decay = 0.9995
epsilon_min = 0.01
alpha = 1 # taxa de aprendizagem inicial
alpha_decay = 0.9995
alpha_min = 0.01 # taxa de aprendizagem minima
gamma = 0.9 # gamma para recompensas
MAX_NUMBER_STEPS = 10000
steps = 0
eta = max(alpha_min, alpha)
eps = max(epsilon_min, epsilon)
total_reward = 0
state = env.reset() # inicializa o ambiente
while steps < MAX_NUMBER_STEPS:
if random.uniform(
0, 1) < eps: # se um numero aleatorio for menor que epsilon
action = env.action_space.sample(
) # escolha uma ação aleatoria (exploration)
else:
action = np.argmax(
Q[state, ]
) # escolha a ação que vai da o maior retorno para aquele estado (exploitation)
new_state, reward, done, info = env.step(
action) # execute a ação e receba uma nova observação do ambiente
total_reward += reward
if done:
target = reward
else:
target = reward + gamma * np.max(Q[new_state, ])
Q[state, action] = (1 - eta) * Q[state, action] + eta * (
target) # atualize a tabela Q com a recompensa recebida pela ação
state = new_state # atualize o estado
if done:
print(
f'steps: {steps} total_reward: {total_reward} alpha: {eta:.4f} epsilon: {eps:.4f} '
)
total_reward = 0
state = env.reset() # inicializa o ambiente
steps += 1
alpha = alpha * alpha_decay
eta = max(alpha_min,
alpha) # faz o decaimento da taxa de aprendizagem
epsilon = epsilon * epsilon_decay
eps = max(epsilon_min, epsilon)
print("Testando...")
# teste apos treinamento
done = False
state = env.reset()
total_reward = 0
while not done:
env.render()
action = np.argmax(Q[state, ])
state, reward, done, info = env.step(action)
print(reward)
time.sleep(1 / 24)
Raw
frozen_q_learning.py
import gym
import time
import numpy as np
import random
env = gym.make('FrozenLake-v0') # selecionar o ambiente
Q = np.ones(
(env.observation_space.n, env.action_space.n), dtype=np.float32
) # construir a tabela Q onde as linhas sao os estados possiveis e as colunas as ações
epsilon = 1
epsilon_decay = 0.99
epsilon_min = 0.01
alpha = 1 # taxa de aprendizagem inicial
alpha_decay = 0.999
alpha_min = 0.01 # taxa de aprendizagem minima
gamma = 0.9999 # gamma para recompensas
MAX_NUMBER_STEPS = 10000
steps = 0
eta = max(alpha_min, alpha)
eps = max(epsilon_min, epsilon)
total_reward = 0
state = env.reset() # inicializa o ambiente
while steps < MAX_NUMBER_STEPS:
if random.uniform(
0, 1) < eps: # se um numero aleatorio for menor que epsilon
action = env.action_space.sample(
) # escolha uma ação aleatoria (exploration)
else:
action = np.argmax(
Q[state, ]
) # escolha a ação que vai da o maior retorno para aquele estado (exploitation)
new_state, reward, done, info = env.step(
action) # execute a ação e receba uma nova observação do ambiente
total_reward += reward
if done:
target = reward
else:
target = reward + gamma * np.max(Q[new_state, ])
Q[state, action] = (1 - eta) * Q[state, action] + eta * (
target) # atualize a tabela Q com a recompensa recebida pela ação
state = new_state # atualize o estado
if done:
print(
f'total_reward: {total_reward} steps: {steps} alpha: {eta} epsilon: {eps}'
)
total_reward = 0
state = env.reset() # inicializa o ambiente
steps += 1
alpha = alpha * alpha_decay
eta = max(alpha_min,
alpha) # faz o decaimento da taxa de aprendizagem
epsilon = epsilon * epsilon_decay
eps = max(epsilon_min, epsilon)
print("Testando...")
# teste apos treinamento
done = False
state = env.reset()
total_reward = 0
while not done:
env.render()
action = np.argmax(Q[state, ])
state, reward, done, info = env.step(action)
print(reward)
time.sleep(1 / 24)
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keras_rl_cartpole_dqn_example.ipynb
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keras_rl_mountaincar_dqn_example.ipynb
{
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"metadata": {
"colab": {
"name": "keras_rl_mountaincar_dqn_example.ipynb",
"provenance": [],
"collapsed_sections": []
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"kernelspec": {
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"cells": [
{
"cell_type": "code",
"metadata": {
"id": "1xKHjbyyLSLi",
"colab_type": "code",
"outputId": "fe6b7d47-aac2-4784-9c43-d9627ed06c16",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"%tensorflow_version 1.x\n",
"! pip install tensorflow==1.14\n",
"! pip install keras-rl\n",
"! pip install h5py"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting tensorflow==1.14\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/de/f0/96fb2e0412ae9692dbf400e5b04432885f677ad6241c088ccc5fe7724d69/tensorflow-1.14.0-cp36-cp36m-manylinux1_x86_64.whl (109.2MB)\n",
"\u001b[K |████████████████████████████████| 109.2MB 97kB/s \n",
"\u001b[?25hRequirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14) (0.33.6)\n",
"Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14) (0.2.2)\n",
"Collecting tensorboard<1.15.0,>=1.14.0\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/91/2d/2ed263449a078cd9c8a9ba50ebd50123adf1f8cfbea1492f9084169b89d9/tensorboard-1.14.0-py3-none-any.whl (3.1MB)\n",
"\u001b[K |████████████████████████████████| 3.2MB 33.8MB/s \n",
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"Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14) (1.1.0)\n",
"Requirement already satisfied: numpy<2.0,>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14) (1.17.5)\n",
"Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14) (1.12.0)\n",
"Collecting tensorflow-estimator<1.15.0rc0,>=1.14.0rc0\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/3c/d5/21860a5b11caf0678fbc8319341b0ae21a07156911132e0e71bffed0510d/tensorflow_estimator-1.14.0-py2.py3-none-any.whl (488kB)\n",
"\u001b[K |████████████████████████████████| 491kB 39.7MB/s \n",
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"Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14) (1.0.8)\n",
"Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14) (3.10.0)\n",
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"Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow==1.14) (3.1.1)\n",
"Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow==1.14) (42.0.2)\n",
"Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow==1.14) (0.16.0)\n",
"Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow==1.14) (2.8.0)\n",
"Installing collected packages: tensorboard, tensorflow-estimator, tensorflow\n",
" Found existing installation: tensorboard 1.15.0\n",
" Uninstalling tensorboard-1.15.0:\n",
" Successfully uninstalled tensorboard-1.15.0\n",
" Found existing installation: tensorflow-estimator 1.15.1\n",
" Uninstalling tensorflow-estimator-1.15.1:\n",
" Successfully uninstalled tensorflow-estimator-1.15.1\n",
" Found existing installation: tensorflow 1.15.0\n",
" Uninstalling tensorflow-1.15.0:\n",
" Successfully uninstalled tensorflow-1.15.0\n",
"Successfully installed tensorboard-1.14.0 tensorflow-1.14.0 tensorflow-estimator-1.14.0\n",
"Collecting keras-rl\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/ab/87/4b57eff8e4bd834cea0a75cd6c58198c9e42be29b600db9c14fafa72ec07/keras-rl-0.4.2.tar.gz (40kB)\n",
"\u001b[K |████████████████████████████████| 40kB 2.8MB/s \n",
"\u001b[?25hRequirement already satisfied: keras>=2.0.7 in /usr/local/lib/python3.6/dist-packages (from keras-rl) (2.2.5)\n",
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"Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from keras>=2.0.7->keras-rl) (1.12.0)\n",
"Requirement already satisfied: keras-preprocessing>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from keras>=2.0.7->keras-rl) (1.1.0)\n",
"Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras>=2.0.7->keras-rl) (2.8.0)\n",
"Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.6/dist-packages (from keras>=2.0.7->keras-rl) (1.17.5)\n",
"Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.6/dist-packages (from keras>=2.0.7->keras-rl) (1.4.1)\n",
"Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from keras>=2.0.7->keras-rl) (3.13)\n",
"Building wheels for collected packages: keras-rl\n",
" Building wheel for keras-rl (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for keras-rl: filename=keras_rl-0.4.2-cp36-none-any.whl size=48379 sha256=90524452b9bfd6deb6d6240251ea5739c97ba6efb36087eefb436f2059558d6e\n",
" Stored in directory: /root/.cache/pip/wheels/7d/4d/84/9254c9f2e8f51865cb0dac8e79da85330c735551d31f73c894\n",
"Successfully built keras-rl\n",
"Installing collected packages: keras-rl\n",
"Successfully installed keras-rl-0.4.2\n",
"Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (2.8.0)\n",
"Requirement already satisfied: numpy>=1.7 in /usr/local/lib/python3.6/dist-packages (from h5py) (1.17.5)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from h5py) (1.12.0)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "6YyJKUdwLlUA",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 462
},
"outputId": "2e6fc84a-34d7-452e-b441-c549403f55e0"
},
"source": [
"import numpy as np\n",
"import gym\n",
"\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Activation, Flatten\n",
"from keras.optimizers import Adam\n",
"\n",
"from rl.agents.dqn import DQNAgent\n",
"from rl.policy import EpsGreedyQPolicy\n",
"from rl.memory import SequentialMemory"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n",
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
"/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
"/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
"/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
"/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
"/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
"/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
"/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Gih5PP3eLsVm",
"colab_type": "code",
"colab": {}
},
"source": [
"ENV_NAME = 'MountainCar-v0'\n",
"\n",
"\n",
"# Get the environment and extract the number of actions.\n",
"env = gym.make(ENV_NAME)\n",
"np.random.seed(123)\n",
"env.seed(123)\n",
"nb_actions = env.action_space.n"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "cfRQ7-j8LuiP",
"colab_type": "code",
"outputId": "2e452de5-348d-41a7-87f0-9e809bfb224c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 598
}
},
"source": [
"\n",
"# Next, we build a very simple model.\n",
"model = Sequential()\n",
"model.add(Flatten(input_shape=(1,) + env.observation_space.shape))\n",
"model.add(Dense(16))\n",
"model.add(Activation('relu'))\n",
"model.add(Dense(16))\n",
"model.add(Activation('relu'))\n",
"model.add(Dense(16))\n",
"model.add(Activation('relu'))\n",
"model.add(Dense(nb_actions))\n",
"model.add(Activation('linear'))\n",
"print(model.summary())"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
"\n",
"Model: \"sequential_1\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"flatten_1 (Flatten) (None, 2) 0 \n",
"_________________________________________________________________\n",
"dense_1 (Dense) (None, 16) 48 \n",
"_________________________________________________________________\n",
"activation_1 (Activation) (None, 16) 0 \n",
"_________________________________________________________________\n",
"dense_2 (Dense) (None, 16) 272 \n",
"_________________________________________________________________\n",
"activation_2 (Activation) (None, 16) 0 \n",
"_________________________________________________________________\n",
"dense_3 (Dense) (None, 16) 272 \n",
"_________________________________________________________________\n",
"activation_3 (Activation) (None, 16) 0 \n",
"_________________________________________________________________\n",
"dense_4 (Dense) (None, 3) 51 \n",
"_________________________________________________________________\n",
"activation_4 (Activation) (None, 3) 0 \n",
"=================================================================\n",
"Total params: 643\n",
"Trainable params: 643\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"None\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "pTeDxvrhL2Q0",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 139
},
"outputId": "da476695-79d0-4bae-b737-8e29aae1a3a7"
},
"source": [
"# Finally, we configure and compile our agent. You can use every built-in Keras optimizer and\n",
"# even the metrics!\n",
"memory = SequentialMemory(limit=50000, window_length=1)\n",
"policy = EpsGreedyQPolicy()\n",
"dqn = DQNAgent(model=model, nb_actions=nb_actions, memory=memory, nb_steps_warmup=10,target_model_update=0.01, policy=policy)\n",
"dqn.compile(Adam(lr=1e-3), metrics=['mae'])"
],
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"text": [
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "P2dQE4m7Yisl",
"colab_type": "code",
"outputId": "15298657-62cf-43c8-b1d2-a07c6efe53d1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 428
}
},
"source": [
"# Okay, now it's time to learn something! We visualize the training here for show, but this\n",
"# slows down training quite a lot. You can always safely abort the training prematurely using\n",
"# Ctrl + C.\n",
"dqn.fit(env, nb_steps=50000, visualize=False, verbose=1)"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": [
"Training for 50000 steps ...\n",
"Interval 1 (0 steps performed)\n",
" 1/10000 [..............................] - ETA: 8:06 - reward: -1.0000"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/rl/memory.py:39: UserWarning: Not enough entries to sample without replacement. Consider increasing your warm-up phase to avoid oversampling!\n",
" warnings.warn('Not enough entries to sample without replacement. Consider increasing your warm-up phase to avoid oversampling!')\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"10000/10000 [==============================] - 34s 3ms/step - reward: -1.0000\n",
"50 episodes - episode_reward: -196.520 [-200.000, -128.000] - loss: 2.426 - mean_absolute_error: 19.995 - mean_q: -29.657\n",
"\n",
"Interval 2 (10000 steps performed)\n",
"10000/10000 [==============================] - 32s 3ms/step - reward: -1.0000\n",
"54 episodes - episode_reward: -185.222 [-200.000, -115.000] - loss: 6.155 - mean_absolute_error: 37.769 - mean_q: -56.012\n",
"\n",
"Interval 3 (20000 steps performed)\n",
"10000/10000 [==============================] - 31s 3ms/step - reward: -1.0000\n",
"57 episodes - episode_reward: -177.807 [-200.000, -100.000] - loss: 6.252 - mean_absolute_error: 38.103 - mean_q: -56.204\n",
"\n",
"Interval 4 (30000 steps performed)\n",
"10000/10000 [==============================] - 32s 3ms/step - reward: -1.0000\n",
"60 episodes - episode_reward: -164.333 [-200.000, -104.000] - loss: 3.864 - mean_absolute_error: 34.893 - mean_q: -51.453\n",
"\n",
"Interval 5 (40000 steps performed)\n",
"10000/10000 [==============================] - 32s 3ms/step - reward: -1.0000\n",
"done, took 160.995 seconds\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7facdc9e84a8>"
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "mmykvkN7aQIy",
"colab_type": "code",
"colab": {}
},
"source": [
"# After training is done, we save the final weights.\n",
"dqn.save_weights('dqn_{}_weights.h5f'.format(ENV_NAME), overwrite=True)\n",
"from google.colab import files\n",
"files.download('dqn_{}_weights.h5f'.format(ENV_NAME)) "
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "r_xyZdh_ZSlK",
"colab_type": "code",
"outputId": "705f44c0-dbe2-4b54-9625-6b244dc9ef8a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 136
}
},
"source": [
"# Finally, evaluate our algorithm for 5 episodes.\n",
"dqn.test(env, nb_episodes=5, visualize=False)"
],
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"text": [
"Testing for 5 episodes ...\n",
"Episode 1: reward: -111.000, steps: 111\n",
"Episode 2: reward: -96.000, steps: 96\n",
"Episode 3: reward: -101.000, steps: 101\n",
"Episode 4: reward: -89.000, steps: 89\n",
"Episode 5: reward: -88.000, steps: 88\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7facbe6bd908>"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
}
]
}
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managerSave.ipynb
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mountaincar_dqn_play.py
import numpy as np
import gym
import time
from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten
from keras.optimizers import Adam
from rl.agents.dqn import DQNAgent
from rl.policy import EpsGreedyQPolicy
from rl.memory import SequentialMemory
ENV_NAME = 'MountainCar-v0'
# Get the environment and extract the number of actions.
env = gym.make(ENV_NAME)
np.random.seed(123)
env.seed(123)
nb_actions = env.action_space.n
# Next, we build a very simple model.
model = Sequential()
model.add(Flatten(input_shape=(1, ) + env.observation_space.shape))
model.add(Dense(16))
model.add(Activation('relu'))
model.add(Dense(16))
model.add(Activation('relu'))
model.add(Dense(16))
model.add(Activation('relu'))
model.add(Dense(nb_actions))
model.add(Activation('linear'))
print(model.summary())
# Finally, we configure and compile our agent. You can use every built-in Keras optimizer and
# even the metrics!
memory = SequentialMemory(limit=50000, window_length=1)
policy = EpsGreedyQPolicy()
dqn = DQNAgent(model=model,
nb_actions=nb_actions,
memory=memory,
nb_steps_warmup=10,
target_model_update=0.01,
policy=policy)
dqn.compile(Adam(lr=1e-3), metrics=['mae'])
dqn.load_weights('dqn_MountainCar-v0_weights.h5f')
dqn.test(env, nb_episodes=15, visualize=True)
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pokemonpix2pix.ipynb
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requirements.txt
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taxi_q_learning.py
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