View training.py
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agent.train_step_counter.assign(0) | |
avg_return = get_average_return(evaluation_env, agent.policy, EVAL_EPISODES) | |
returns = [avg_return] | |
for _ in range(NUMBER_ITERATION): | |
for _ in range(COLLECTION_STEPS): | |
experience_replay.timestamp_data(train_env, agent.collect_policy) |
View ExperienceReply.py
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class ExperienceReply(object): | |
def __init__(self, agent, enviroment): | |
self._replay_buffer = TFUniformReplayBuffer( | |
data_spec=agent.collect_data_spec, | |
batch_size=enviroment.batch_size, | |
max_length=50000) | |
self._random_policy = RandomTFPolicy(train_env.time_step_spec(), | |
enviroment.action_spec()) | |
View average_return.py
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def get_average_return(environment, policy, episodes=10): | |
total_return = 0.0 | |
for _ in range(episodes): | |
time_step = environment.reset() | |
episode_return = 0.0 | |
while not time_step.is_last(): | |
action_step = policy.action(time_step) |
View DQNAgent.py
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counter = tf.Variable(0) | |
agent = DqnAgent( | |
train_env.time_step_spec(), | |
train_env.action_spec(), | |
q_network = q_network, | |
optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=1e-3), | |
td_errors_loss_fn = common.element_wise_squared_loss, | |
train_step_counter = counter) |
View qnetwork.py
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hidden_layers = (100,) | |
q_network = QNetwork( | |
train_env.observation_spec(), | |
train_env.action_spec(), | |
fc_layer_params=hidden_layers) |
View qnet_constructor.py
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class QNetwork(network.Network): | |
"""Feed Forward network.""" | |
def __init__(self, | |
input_tensor_spec, | |
action_spec, | |
preprocessing_layers=None, | |
preprocessing_combiner=None, | |
conv_layer_params=None, | |
fc_layer_params=(75, 40), |
View enviroments.py
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train_env = suite_gym.load('CartPole-v0') | |
evaluation_env = suite_gym.load('CartPole-v0') | |
print('Observation Spec:') | |
print(train_env.time_step_spec().observation) | |
print('Reward Spec:') | |
print(train_env.time_step_spec().reward) | |
print('Action Spec:') |
View imports_globals.py
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import base64 | |
import imageio | |
import matplotlib | |
import matplotlib.pyplot as plt | |
import tensorflow as tf | |
from tf_agents.agents.dqn.dqn_agent import DqnAgent | |
from tf_agents.networks.q_network import QNetwork |
View usage.py
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dataProcessor = DataProcessor(32, 300, 500, list_dataset) | |
dataProcessor.load_process() | |
image_batch, label_batch = dataProcessor.get_batch() |
View load_process.py
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def load_process(self, shuffle_size = 1000): | |
self.loaded_dataset = self.dataset.map(self._load_labeled_data, num_parallel_calls=tf.data.experimental.AUTOTUNE) | |
self.loaded_dataset = self.loaded_dataset.cache() | |
# Shuffle data and create batches | |
self.loaded_dataset = self.loaded_dataset.shuffle(buffer_size=shuffle_size) | |
self.loaded_dataset = self.loaded_dataset.repeat() | |
self.loaded_dataset = self.loaded_dataset.batch(self.batch_size) |
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