96 lines
3.5 KiB
Python
96 lines
3.5 KiB
Python
#!/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 gym
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import numpy as np
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import time
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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.dqn as policy # TODO: fix imports as zhusuan so that only need to import to policy
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import tianshou.core.value_function.action_value as value_function
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import tianshou.data.replay_buffer.proportional as proportional
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import tianshou.data.replay_buffer.rank_based as rank_based
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import tianshou.data.replay_buffer.naive as naive
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import tianshou.data.replay_buffer.Replay as Replay
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# TODO: why this solves cartpole even without training?
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if __name__ == '__main__':
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env = gym.make('CartPole-v0')
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observation_dim = env.observation_space.shape
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action_dim = env.action_space.n
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clip_param = 0.2
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num_batches = 10
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batch_size = 512
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seed = 0
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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)
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def my_network():
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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_values = tf.layers.dense(net, action_dim, activation=None)
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return None, action_values # no policy head
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### 2. build policy, loss, optimizer
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dqn = value_function.DQN(my_network, observation_placeholder=observation_ph, weight_update=100)
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pi = policy.DQN(dqn)
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dqn_loss = losses.qlearning(dqn)
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total_loss = dqn_loss
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global_step = tf.Variable(0, name='global_step', trainable=False)
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optimizer = tf.train.AdamOptimizer(1e-4)
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train_op = optimizer.minimize(total_loss, var_list=dqn.trainable_variables, global_step=tf.train.get_global_step())
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# replay_memory = naive.NaiveExperience({'size': 1000})
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replay_memory = rank_based.RankBasedExperience({'size': 30})
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# replay_memory = proportional.PropotionalExperience({'size': 100, 'batch_size': 10})
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data_collector = Replay.Replay(replay_memory, env, pi, [advantage_estimation.ReplayMemoryQReturn(1, dqn)], [dqn])
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### 3. define data collection
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# data_collector = Batch(env, pi, [advantage_estimation.nstep_q_return(1, dqn)], [dqn])
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### 4. start training
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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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# assign actor to 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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#TODO : repeat_num shoulde be defined in some configuration files
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repeat_num = 100
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for i in range(repeat_num):
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# collect data
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# data_collector.collect(nums=50)
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data_collector.collect(num_episodes=50, epsilon_greedy= (repeat_num - i + 0.0) / repeat_num)
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# print current return
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print('Epoch {}:'.format(i))
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data_collector.statistics()
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# update network
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for _ in range(num_batches):
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feed_dict = data_collector.next_batch(batch_size, tf.train.global_step(sess, global_step))
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sess.run(train_op, feed_dict=feed_dict)
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print('Elapsed time: {:.1f} min'.format((time.time() - start_time) / 60))
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