98 lines
4.0 KiB
Python
Executable File
98 lines
4.0 KiB
Python
Executable File
#!/usr/bin/env python
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from __future__ import absolute_import
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import tensorflow as tf
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import time
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import numpy as np
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# our lib imports here! It's ok to append path in examples
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import sys
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sys.path.append('..')
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from tianshou.core import losses
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from tianshou.data.batch import Batch
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import tianshou.data.advantage_estimation as advantage_estimation
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import tianshou.core.policy.stochastic as policy # TODO: fix imports as zhusuan so that only need to import to policy
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from rllab.envs.box2d.cartpole_env import CartpoleEnv
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from rllab.envs.normalized_env import normalize
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# for tutorial purpose, placeholders are explicitly appended with '_ph' suffix
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if __name__ == '__main__':
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env = normalize(CartpoleEnv())
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observation_dim = env.observation_space.shape
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action_dim = env.action_space.flat_dim
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clip_param = 0.2
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num_batches = 10
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batch_size = 128
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seed = 10
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np.random.seed(seed)
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tf.set_random_seed(seed)
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### 1. build network with pure tf
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observation_ph = tf.placeholder(tf.float32, shape=(None,) + observation_dim) # network input
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def my_policy():
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net = tf.layers.dense(observation_ph, 32, activation=tf.nn.tanh)
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net = tf.layers.dense(net, 32, activation=tf.nn.tanh)
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action_mean = tf.layers.dense(net, action_dim, activation=None)
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action_logstd = tf.get_variable('action_logstd', shape=(action_dim, ))
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# value = tf.layers.dense(net, 1, activation=None)
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return action_mean, action_logstd, None # None value head
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# TODO: current implementation of passing function or overriding function has to return a value head
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# to allow network sharing between policy and value networks. This makes 'policy' and 'value_function'
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# imbalanced semantically (though they are naturally imbalanced since 'policy' is required to interact
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# with the environment and 'value_function' is not). I have an idea to solve this imbalance, which is
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# not based on passing function or overriding function.
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### 2. build policy, losses, optimizers
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pi = policy.Normal(my_policy, observation_placeholder=observation_ph, weight_update=0)
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# action = tf.placeholder(dtype=tf.float32, shape=(None, action_dim)) # batch of integer actions
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# advantage = tf.placeholder(dtype=tf.float32, shape=(None,)) # advantage values used in the Gradients
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ppo_loss_clip = losses.ppo_clip(pi, clip_param) # TongzhengRen: losses.vpg ... management of placeholders and feed_dict
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total_loss = ppo_loss_clip
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optimizer = tf.train.AdamOptimizer(1e-4)
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train_op = optimizer.minimize(total_loss, var_list=pi.trainable_variables)
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### 3. define data collection
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training_data = Batch(env, pi, advantage_estimation.full_return) # YouQiaoben: finish and polish Batch, advantage_estimation.gae_lambda as in PPO paper
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# ShihongSong: Replay(), see dqn_example.py
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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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# init = tf.global_variables_initializer()
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config = tf.ConfigProto()
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config.gpu_options.allow_growth = True
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with tf.Session(config=config) as sess:
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sess.run(tf.global_variables_initializer())
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# sync pi and pi_old
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pi.sync_weights() # TODO: automate this for policies with target network
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start_time = time.time()
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for i in range(100): # until some stopping criterion met...
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# collect data
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training_data.collect(num_episodes=20) # YouQiaoben, ShihongSong
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# print current return
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print('Epoch {}:'.format(i))
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training_data.statistics()
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# update network
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for _ in range(num_batches):
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feed_dict = training_data.next_batch(batch_size) # YouQiaoben, ShihongSong
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sess.run(train_op, feed_dict=feed_dict)
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# assigning pi to pi_old
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pi.update_weights()
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print('Elapsed time: {:.1f} min'.format((time.time() - start_time) / 60)) |