##################
saver = tf.train.Saver()
init = tf.initialize_all_variables()
summary = tf.merge_all_summaries()
logger = tf.train.SummaryWriter(OUT_DIR, sess.graph)
# initialize mdp state structure
mdp = MDP_state(STATE_SIZE, FRAMES)
# initialize replay buffer
R = ReplayBuffer(MDP_STATE_SIZE, ACTION_SIZE, BUFFER_SIZE)
buf = R.LoadBuffer(OUT_DIR + BUFFER_FILE)
if buf:
EXP_PROB = EPSILON
populated = R.GetOccupency()
print("Replay buffer loaded from disk, occupied: " + str(populated))
else:
print("Creating new replay buffer")
# load saved model
ckpt = tf.train.get_checkpoint_state(OUT_DIR)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print("Model loaded from disk")
# define action discretization
#max_a = env.action_space.high[0]
#min_a = env.action_space.low[0]
n = OUnoise(1, 0.5, 1)
ann_fric = (1-EPSILON)/ANNEALING
EXP_PROB = 1
# initialize environment
env = gym.make(ENVIRONMENT)
# initialize mdp state structure
mdp = MDP_state(STATE_SIZE, FRAMES)
# initialize replay buffer
R = ReplayBuffer(MDP_STATE_SIZE, 1, BUFFER_SIZE)
buf = R.LoadBuffer(OUT_DIR+BUFFER_FILE)
if buf:
EXP_PROB = EPSILON
populated = R.GetOccupency()
print("Replay buffer loaded from disk, occupied: " + str(populated))
else:
print("Creating new replay buffer")
# load saved model
ckpt = tf.train.get_checkpoint_state(OUT_DIR)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess,ckpt.model_checkpoint_path)
print("Model loaded from disk")
# define action discretization
max_a = env.action_space.high[0]
min_a = env.action_space.low[0]
act = actions(ACTION_SIZE, max_a)
