86 lines
3.4 KiB
Python
86 lines
3.4 KiB
Python
#!/usr/bin/env python
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import tensorflow as tf
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import numpy as np
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import time
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import gym
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# our lib imports here!
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import sys
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sys.path.append('..')
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import tianshou.core.losses as losses
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from tianshou.data.replay import Replay
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import tianshou.data.advantage_estimation as advantage_estimation
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import tianshou.core.policy as policy
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def policy_net(observation, action_dim):
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"""
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Constructs the policy network. NOT NEEDED IN THE LIBRARY! this is pure tf
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:param observation: Placeholder for the observation. A tensor of shape (bs, x, y, channels)
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:param action_dim: int. The number of actions.
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:param scope: str. Specifying the scope of the variables.
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"""
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net = tf.layers.conv2d(observation, 16, 8, 4, 'valid', activation=tf.nn.relu)
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net = tf.layers.conv2d(net, 32, 4, 2, 'valid', activation=tf.nn.relu)
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net = tf.layers.flatten(net)
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net = tf.layers.dense(net, 256, activation=tf.nn.relu)
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q_values = tf.layers.dense(net, action_dim)
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return q_values
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if __name__ == '__main__':
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env = gym.make('PongNoFrameskip-v4')
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observation_dim = env.observation_space.shape
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action_dim = env.action_space.n
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# 1. build network with pure tf
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observation = tf.placeholder(tf.float32, shape=(None,) + observation_dim) # network input
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with tf.variable_scope('q_net'):
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q_values = policy_net(observation, action_dim)
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train_var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES) # TODO: better management of TRAINABLE_VARIABLES
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with tf.variable_scope('target_net'):
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q_values_target = policy_net(observation, action_dim)
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# 2. build losses, optimizers
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q_net = policy.DQN(q_values, observation_placeholder=observation) # YongRen: policy.DQN
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target_net = policy.DQN(q_values_target, observation_placeholder=observation)
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action = tf.placeholder(dtype=tf.int32, shape=[None]) # batch of integer actions
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target = tf.placeholder(dtype=tf.float32, shape=[None]) # target value for DQN
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dqn_loss = losses.dqn_loss(action, target, q_net) # TongzhengRen
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total_loss = dqn_loss
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optimizer = tf.train.AdamOptimizer(1e-3)
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train_op = optimizer.minimize(total_loss, var_list=train_var_list)
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# 3. define data collection
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training_data = Replay(env, q_net, advantage_estimation.qlearning_target(target_net)) #
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# ShihongSong: Replay(env, pi, advantage_estimation.qlearning_target(target_network)), use your ReplayMemory, interact as follows. Simplify your advantage_estimation.dqn to run before YongRen's DQN
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# maybe a dict to manage the elements to be collected
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# 4. start training
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with tf.Session() as sess:
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sess.run(tf.global_variables_initializer())
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minibatch_count = 0
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collection_count = 0
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while True: # until some stopping criterion met...
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# collect data
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training_data.collect() # ShihongSong
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collection_count += 1
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print('Collected {} times.'.format(collection_count))
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# update network
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data = training_data.next_batch(64) # YouQiaoben, ShihongSong
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# TODO: auto managing of the placeholders? or add this to params of data.Batch
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sess.run(train_op, feed_dict={observation: data['observations'], action: data['actions'], target: data['target']})
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minibatch_count += 1
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print('Trained {} minibatches.'.format(minibatch_count))
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# TODO: assigning pi to pi_old is not implemented yet |