work for tl2 tf2

examples/reinforcement_learning/tutorial_atari_pong.py

@@ -34,11 +34,7 @@
 import gym
 import tensorlayer as tl
 
-## enable eager mode
-tf.enable_eager_execution()
 
-
-tf.logging.set_verbosity(tf.logging.DEBUG) # enable logging
 tl.logging.set_verbosity(tl.logging.DEBUG)
 
 # hyper-parameters
@@ -52,7 +48,7 @@
 render = False  # display the game environment
 # resume = True         # load existing policy network
 model_file_name = "model_pong"
-np.set_printoptions(threshold=np.nan)
+np.set_printoptions(threshold=np.inf)
 
 
 def prepro(I):
@@ -73,10 +69,7 @@ def prepro(I):
 episode_number = 0
 
 xs, ys, rs = [], [], []
-# observation for training and inference
-# t_states = tf.placeholder(tf.float32, shape=[None, D])
 # policy network
-
 def get_model(inputs_shape):
     ni = tl.layers.Input(inputs_shape)
     nn = tl.layers.Dense(n_units=H, act=tf.nn.relu, name='hidden')(ni)
@@ -85,22 +78,9 @@ def get_model(inputs_shape):
     return M
 model = get_model([None, D])
 train_weights = model.trainable_weights
-# probs = model(t_states, is_train=True).outputs
-# sampling_prob = tf.nn.softmax(probs)
-
-# t_actions = tf.placeholder(tf.int32, shape=[None])
-# t_discount_rewards = tf.placeholder(tf.float32, shape=[None])
-# loss = tl.rein.cross_entropy_reward_loss(probs, t_actions, t_discount_rewards)
-optimizer = tf.train.RMSPropOptimizer(learning_rate, decay_rate)#.minimize(loss)
-
-# with tf.Session() as sess:
-#     sess.run(tf.global_variables_initializer())
-    # if resume: TODO
-    #     load_params = tl.files.load_npz(name=model_file_name+'.npz')
-    #     tl.files.assign_params(sess, load_params, network)
-    # tl.files.load_and_assign_npz(sess, model_file_name + '.npz', network)
-    # network.print_params()
-    # network.print_layers()
+
+optimizer = tf.optimizers.RMSprop(lr=learning_rate, decay=decay_rate)
+
 model.train() # set model to train mode (in case you add dropout into the model)
 
 start_time = time.time()
@@ -114,14 +94,12 @@ def get_model(inputs_shape):
     x = x.reshape(1, D)
     prev_x = cur_x
 
-    # prob = sess.run(sampling_prob, feed_dict={t_states: x})
-    _prob = model(x).outputs
+    _prob = model(x)
     prob = tf.nn.softmax(_prob)
 
     # action. 1: STOP  2: UP  3: DOWN
         # action = np.random.choice([1,2,3], p=prob.flatten())
         # action = tl.rein.choice_action_by_probs(prob.flatten(), [1, 2, 3])
-    # action = np.random.choice([1,2,3], p=prob.numpy())
     action = tl.rein.choice_action_by_probs(prob[0].numpy(), [1, 2, 3])
 
     observation, reward, done, _ = env.step(action)
@@ -145,12 +123,8 @@ def get_model(inputs_shape):
 
             xs, ys, rs = [], [], []
 
-            # sess.run(train_op, feed_dict={t_states: epx, t_actions: epy, t_discount_rewards: disR})
-                # t_actions = tf.placeholder(tf.int32, shape=[None])
-                # t_discount_rewards = tf.placeholder(tf.float32, shape=[None])
-                # loss = tl.rein.cross_entropy_reward_loss(probs, t_actions, t_discount_rewards)
             with tf.GradientTape() as tape:
-                _prob = model(epx).outputs
+                _prob = model(epx)
                 _loss = tl.rein.cross_entropy_reward_loss(_prob, epy, disR)
             grad = tape.gradient(_loss, train_weights)
             optimizer.apply_gradients(zip(grad, train_weights))