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initial data_collector. working on examples/dqn_replay.py to run

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haoshengzou 2018-03-04 21:29:58 +08:00
parent 54a7b1343d
commit 2a2274aeea
4 changed files with 78 additions and 31 deletions
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2
tianshou/core/policy/base.py
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@ -16,7 +16,7 @@ class PolicyBase(object):
"""
base class for policy. only provides `act` method with exploration
"""
def act(self, observation):
def act(self, observation, my_feed_dict):
raise NotImplementedError()

21
tianshou/core/policy/dqn.py
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@ -9,7 +9,7 @@ class DQN(PolicyBase):
"""
use DQN from value_function as a member
"""
def __init__(self, dqn):
def __init__(self, dqn, epsilon_train=0.1, epsilon_test=0.05):
self.action_value = dqn
self._argmax_action = tf.argmax(dqn.value_tensor_all_actions, axis=1)
self.weight_update = dqn.weight_update
@ -18,20 +18,29 @@ class DQN(PolicyBase):
else:
self.interaction_count = -1
def act(self, observation, my_feed_dict):
self.epsilon_train = epsilon_train
self.epsilon_test = epsilon_test
def act(self, observation, my_feed_dict={}):
sess = tf.get_default_session()
if self.weight_update > 1:
if self.interaction_count % self.weight_update == 0:
self.update_weights()
feed_dict = {self.action_value._observation_placeholder: observation[None]}
feed_dict.update(my_feed_dict)
action = sess.run(self._argmax_action, feed_dict=feed_dict)
if np.random.rand() < self.epsilon_train:
pass
if self.weight_update > 0:
self.interaction_count += 1
return np.squeeze(action)
def act_test(self, observation, my_feed_dict={}):
pass
@property
def q_net(self):
return self.action_value
@ -50,4 +59,10 @@ class DQN(PolicyBase):
:return:
"""
if self.action_value.weight_update_ops is not None:
self.action_value.update_weights()
self.action_value.update_weights()
def set_epsilon_train(self, epsilon):
self.epsilon_train = epsilon
def set_epsilon_test(self, epsilon):
self.epsilon_test = epsilon

14
tianshou/data/advantage_estimation.py
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@ -101,7 +101,7 @@ class ddpg_return:
pass
class ReplayMemoryQReturn:
class nstep_q_return:
"""
compute the n-step return for Q-learning targets
"""
@ -111,7 +111,7 @@ class ReplayMemoryQReturn:
self.use_target_network = use_target_network
# TODO : we should transfer the tf -> numpy/python -> tf into a monolithic compute graph in tf
def __call__(self, buffer, indexes =None):
def __call__(self, buffer, index=None):
"""
:param buffer: buffer with property index and data. index determines the current content in `buffer`.
:param index: (sampled) index to be computed. Defaults to all the data in `buffer`. Not necessarily in order within
@ -119,7 +119,7 @@ class ReplayMemoryQReturn:
:return: dict with key 'return' and value the computed returns corresponding to `index`.
"""
qvalue = self.action_value._value_tensor_all_actions
indexes = indexes or buffer.index
index = index or buffer.index
episodes = buffer.data
discount_factor = 0.99
returns = []
@ -128,13 +128,11 @@ class ReplayMemoryQReturn:
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
for episode_index in range(len(indexes)):
index = indexes[episode_index]
for episode_index in range(len(index)):
index = index[episode_index]
if index:
episode = episodes[episode_index]
episode_q = []
if not episode[-1][DONE]:
logging.warning('Computing Q return on episode {} with no terminal state.'.format(episode_index))
for i in index:
current_discount_factor = 1
@ -155,4 +153,4 @@ class ReplayMemoryQReturn:
returns.append(episode_q)
else:
returns.append([])
return {'TD-lambda': returns}
return {'return': returns}

72
tianshou/data/data_collector.py
View File

@ -1,3 +1,7 @@
import numpy as np
import logging
import itertools
from .replay_buffer.base import ReplayBufferBase
class DataCollector(object):
@ -11,30 +15,28 @@ class DataCollector(object):
self.process_functions = process_functions
self.managed_networks = managed_networks
self.data = {}
self.data_batch = {}
self.required_placeholders = {}
for net in self.managed_networks:
self.required_placeholders.update(net.managed_placeholders)
self.require_advantage = 'advantage' in self.required_placeholders.keys()
if isinstance(self.data_buffer, ReplayBufferBase): # process when sampling minibatch
self.process_mode = 'minibatch'
self.process_mode = 'sample'
else:
self.process_mode = 'batch'
self.process_mode = 'full'
self.current_observation = self.env.reset()
def collect(self, num_timesteps=1, num_episodes=0, exploration=None, my_feed_dict={}):
def collect(self, num_timesteps=1, num_episodes=0, my_feed_dict={}):
assert sum([num_timesteps > 0, num_episodes > 0]) == 1,\
"One and only one collection number specification permitted!"
if num_timesteps > 0:
for _ in range(num_timesteps):
action_vanilla = self.policy.act(self.current_observation, my_feed_dict=my_feed_dict)
if exploration:
action = exploration(action_vanilla)
else:
action = action_vanilla
action = self.policy.act(self.current_observation, my_feed_dict=my_feed_dict)
next_observation, reward, done, _ = self.env.step(action)
self.data_buffer.add((self.current_observation, action, reward, done))
self.current_observation = next_observation
@ -44,24 +46,56 @@ class DataCollector(object):
observation = self.env.reset()
done = False
while not done:
action_vanilla = self.policy.act(observation, my_feed_dict=my_feed_dict)
if exploration:
action = exploration(action_vanilla)
else:
action = action_vanilla
action = self.policy.act(observation, my_feed_dict=my_feed_dict)
next_observation, reward, done, _ = self.env.step(action)
self.data_buffer.add((observation, action, reward, done))
observation = next_observation
def next_batch(self, batch_size):
if self.process_mode == 'full':
for processor in self.process_functions:
self.data.update(processor(self.data_buffer))
def next_batch(self, batch_size, standardize_advantage=True):
sampled_index = self.data_buffer.sample(batch_size)
if self.process_mode == 'minibatch':
pass
if self.process_mode == 'sample':
for processor in self.process_functions:
self.data_batch.update(processor(self.data_buffer, index=sampled_index))
# flatten rank-2 list to numpy array, construct feed_dict
feed_dict = {}
frame_key_map = {'observation': 0, 'action': 1, 'reward': 2, 'done_flag': 3}
for key, placeholder in self.required_placeholders.items():
if key in frame_key_map.keys(): # access raw_data
frame_index = frame_key_map[key]
flattened = []
for index_episode, data_episode in zip(sampled_index, self.data_buffer.data):
for i in index_episode:
flattened.append(data_episode[i][frame_index])
feed_dict[placeholder] = np.array(flattened)
elif key in self.data_batch.keys(): # access processed minibatch data
flattened = list(itertools.chain.from_iterable(self.data_batch[key]))
feed_dict[placeholder] = np.array(flattened)
elif key in self.data.keys(): # access processed full data
flattened = [0.] * batch_size # float
i_in_batch = 0
for index_episode, data_episode in zip(sampled_index, self.data[key]):
for i in index_episode:
flattened[i_in_batch] = data_episode[i]
i_in_batch += 1
feed_dict[placeholder] = np.array(flattened)
else:
raise TypeError('Placeholder {} has no value to feed!'.format(str(placeholder.name)))
return
if standardize_advantage:
if self.require_advantage:
advantage_value = feed_dict[self.required_placeholders['advantage']]
advantage_mean = np.mean(advantage_value)
advantage_std = np.std(advantage_value)
if advantage_std < 1e-3:
logging.warning('advantage_std too small (< 1e-3) for advantage standardization. may cause numerical issues')
feed_dict[self.required_placeholders['advantage']] = (advantage_value - advantage_mean) / advantage_std
return feed_dict
def statistics(self):
pass