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136 | from __future__ import division
import argparse
from PIL import Image
import numpy as np
import easydict # added
import gym
from gym import wrappers # 追加
from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten, Convolution2D, Permute
from keras.optimizers import Adam
import keras.backend as K
from rl.agents.dqn import DQNAgent
from rl.policy import LinearAnnealedPolicy, BoltzmannQPolicy, EpsGreedyQPolicy
from rl.memory import SequentialMemory
from rl.core import Processor
from rl.callbacks import FileLogger, ModelIntervalCheckpoint
INPUT_SHAPE = (84, 84)
WINDOW_LENGTH = 4
class AtariProcessor(Processor):
def process_observation(self, observation):
assert observation.ndim == 3 # (height, width, channel)
img = Image.fromarray(observation)
img = img.resize(INPUT_SHAPE).convert('L') # resize and convert to grayscale
processed_observation = np.array(img)
assert processed_observation.shape == INPUT_SHAPE
return processed_observation.astype('uint8') # saves storage in experience memory
def process_state_batch(self, batch):
# We could perform this processing step in `process_observation`. In this case, however,
# we would need to store a `float32` array instead, which is 4x more memory intensive than
# an `uint8` array. This matters if we store 1M observations.
processed_batch = batch.astype('float32') / 255.
return processed_batch
def process_reward(self, reward):
return np.clip(reward, -1., 1.)
## argparse failes. Use easydict instead
## see https://qiita.com/LittleWat/items/6e56857e1f97c842b261
#parser = argparse.ArgumentParser()
#parser.add_argument('--mode', choices=['train', 'test'], default='train')
#parser.add_argument('--env-name', type=str, default='BreakoutDeterministic-v4')
#parser.add_argument('--weights', type=str, default=None)
#args = parser.parse_args()
args = easydict.EasyDict({ # use this instead of argparse
"mode": "train",
"env_name": "BreakoutDeterministic-v4",
"weight": None,
})
# Get the environment and extract the number of actions.
env = gym.make(args.env_name)
env = wrappers.Monitor(env, './', force=True) # save animations
np.random.seed(123)
env.seed(123)
nb_actions = env.action_space.n
# Next, we build our model. We use the same model that was described by Mnih et al. (2015).
input_shape = (WINDOW_LENGTH,) + INPUT_SHAPE
model = Sequential()
if K.image_dim_ordering() == 'tf':
# (width, height, channels)
model.add(Permute((2, 3, 1), input_shape=input_shape))
elif K.image_dim_ordering() == 'th':
# (channels, width, height)
model.add(Permute((1, 2, 3), input_shape=input_shape))
else:
raise RuntimeError('Unknown image_dim_ordering.')
model.add(Convolution2D(32, (8, 8), strides=(4, 4)))
model.add(Activation('relu'))
model.add(Convolution2D(64, (4, 4), strides=(2, 2)))
model.add(Activation('relu'))
model.add(Convolution2D(64, (3, 3), strides=(1, 1)))
model.add(Activation('relu'))
model.add(Flatten())
model.add(Dense(512))
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=1000000, window_length=WINDOW_LENGTH)
processor = AtariProcessor()
# Select a policy. We use eps-greedy action selection, which means that a random action is selected
# with probability eps. We anneal eps from 1.0 to 0.1 over the course of 1M steps. This is done so that
# the agent initially explores the environment (high eps) and then gradually sticks to what it knows
# (low eps). We also set a dedicated eps value that is used during testing. Note that we set it to 0.05
# so that the agent still performs some random actions. This ensures that the agent cannot get stuck.
policy = LinearAnnealedPolicy(EpsGreedyQPolicy(), attr='eps', value_max=1., value_min=.1, value_test=.05,
nb_steps=1000000)
# The trade-off between exploration and exploitation is difficult and an on-going research topic.
# If you want, you can experiment with the parameters or use a different policy. Another popular one
# is Boltzmann-style exploration:
# policy = BoltzmannQPolicy(tau=1.)
# Feel free to give it a try!
dqn = DQNAgent(model=model, nb_actions=nb_actions, policy=policy, memory=memory,
processor=processor, nb_steps_warmup=50000, gamma=.99, target_model_update=10000,
train_interval=4, delta_clip=1.)
dqn.compile(Adam(lr=.00025), metrics=['mae'])
if args.mode == 'train':
# Okay, now it's time to learn something! We capture the interrupt exception so that training
# can be prematurely aborted. Notice that you can the built-in Keras callbacks!
weights_filename = 'dqn_{}_weights.h5f'.format(args.env_name)
checkpoint_weights_filename = 'dqn_' + args.env_name + '_weights_{step}.h5f'
log_filename = 'dqn_{}_log.json'.format(args.env_name)
callbacks = [ModelIntervalCheckpoint(checkpoint_weights_filename, interval=250000)]
callbacks += [FileLogger(log_filename, interval=100)]
dqn.fit(env, callbacks=callbacks, nb_steps=1750000, log_interval=10000)
# After training is done, we save the final weights one more time.
dqn.save_weights(weights_filename, overwrite=True)
# Finally, evaluate our algorithm for 10 episodes.
dqn.test(env, nb_episodes=10, visualize=False)
elif args.mode == 'test':
weights_filename = 'dqn_{}_weights.h5f'.format(args.env_name)
if args.weights:
weights_filename = args.weights
dqn.load_weights(weights_filename)
dqn.test(env, nb_episodes=10, visualize=True)
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