linear model

hw1/behavior_clone.py

+import roboschool
+import numpy as np
+import tensorflow as tf
+import pickle
+import os
+from hw1 import tf_util
+import gym
+
+envname = 'RoboschoolAnt-v1'
+
+with open(os.path.join('expert_data', envname + '.pkl'), 'rb') as f:
+    expert_data = pickle.load(f)
+
+x = tf.placeholder(tf.float32, shape=[None, expert_data['observations'].shape[1]])
+y_true = tf.placeholder(tf.float32, shape=[None, expert_data['actions'].shape[1]])
+
+hidden = tf.layers.Dense(units=64, activation=tf.nn.relu)
+model = tf.layers.Dense(units=expert_data['actions'].shape[1], use_bias=False)(hidden)
+y_pred = model(x)
+
+loss = tf.losses.mean_squared_error(labels=y_true, predictions=y_pred)
+
+optimizer = tf.train.AdamOptimizer(0.001)
+train = optimizer.minimize(loss)
+'''
+(x_train, y_train),(x_test, y_test) = mnist.load_data()
+x_train, x_test = x_train / 255.0, x_test / 255.0
+
+model = tf.keras.models.Sequential([
+  tf.keras.layers.Flatten(input_shape=(28, 28)),
+  tf.keras.layers.Dense(512, activation=tf.nn.relu),
+  tf.keras.layers.Dropout(0.2),
+  tf.keras.layers.Dense(10, activation=tf.nn.softmax)
+])
+model.compile(optimizer='adam',
+              loss='sparse_categorical_crossentropy',
+              metrics=['accuracy'])
+
+model.fit(x_train, y_train, epochs=5)
+model.evaluate(x_test, y_test)
+'''
+with tf.Session() as sess:
+    tf_util.initialize()
+
+    for i in range(1000):
+        _, loss_value = sess.run(
+            (train, loss),
+            feed_dict={x: expert_data['observations'], y_true: expert_data['actions']})
+        print(loss_value)
+
+    # Play
+    env = gym.make(envname)
+    max_steps = env.spec.timestep_limit
+    obs = env.reset()
+    done = False
+    totalr = 0.
+    steps = 0
+    while not done:
+        action = sess.run(y_pred, feed_dict={x: obs[np.newaxis, :]})
+        # observations.append(obs)
+        # actions.append(action)
+        obs, r, done, _ = env.step(np.squeeze(action))
+        totalr += r
+        steps += 1
+        env.render()
+        if steps % 100 == 0: print("%i/%i" % (steps, max_steps))
+        if steps >= max_steps:
+            break
+    # print(sess.run(y_pred))
+
+np.mean((expert_data['actions'] - np.mean(expert_data['actions'], axis=0)) ** 2)