import tensorflow as tf

REPLACE_ITER_C = 1500
MEMORY_CAPACITY = 200000
BATCH_SIZE = 32
DISPLAY_THRESHOLD = 100  # display until the running reward > 100
DATA_PATH = './data'
LOAD_MODEL = False
SAVE_MODEL_ITER = 100000
RENDER = False
OUTPUT_GRAPH = False
ENV_NAME = 'BipedalWalker-v2'

GLOBAL_STEP = tf.Variable(0, trainable=False)
INCREASE_GS = GLOBAL_STEP.assign(tf.add(GLOBAL_STEP, 1))
LR_A = tf.train.exponential_decay(LR_A, GLOBAL_STEP, 10000, .97, staircase=True)
LR_C = tf.train.exponential_decay(LR_C, GLOBAL_STEP, 10000, .97, staircase=True)
END_POINT = (200 - 10) * (14/30)    # from game

env = gym.make(ENV_NAME)
env.seed(1)

STATE_DIM = env.observation_space.shape[0]  # 24
ACTION_DIM = env.action_space.shape[0]  # 4
ACTION_BOUND = env.action_space.high    # [1, 1, 1, 1]

# all placeholder for tf
with tf.name_scope('S'):
    S = tf.placeholder(tf.float32, shape=[None, STATE_DIM], name='s')
with tf.name_scope('R'):
