def EnvFactory(env_name):
parts = env_name.split(':')
if len(parts) > 2:
raise ValueError('Incorrect environment name %s' % env_name)
if parts[0] == '2048':
env = game2048.Game2048()
else:
env = gym.make(parts[0])
if len(parts) == 2:
for letter in parts[1]:
if letter == 'L':
env = atari_wrappers.EpisodicLifeEnv(env)
elif letter == 'N':
env = atari_wrappers.NoopResetEnv(env, noop_max=30)
elif letter == 'S':
env = atari_wrappers.MaxAndSkipEnv(env, skip=4)
elif letter == 'X':
env = atari_wrappers.StackAndSkipEnv(env, skip=3)
elif letter == 'F':
env = atari_wrappers.FireResetEnv(env)
elif letter == 'C':
env = atari_wrappers.ClippedRewardsWrapper(env)
elif letter == 'P':
env = atari_wrappers.ProcessFrame84(env)
else:
raise ValueError('Unexpected code of wrapper %s' % letter)
return env
def PrimaryAtariWrap(env, clip_rewards=True):
assert 'NoFrameskip' in env.spec.id
# This wrapper holds the same action for <skip> frames and outputs
# the maximal pixel value of 2 last frames (to handle blinking
# in some envs)
env = atari_wrappers.MaxAndSkipEnv(env, skip=4)
# This wrapper sends done=True when each life is lost
# (not all the 5 lives that are givern by the game rules).
# It should make easier for the agent to understand that losing is bad.
env = atari_wrappers.EpisodicLifeEnv(env)
# This wrapper laucnhes the ball when an episode starts.
# Without it the agent has to learn this action, too.
# Actually it can but learning would take longer.
env = atari_wrappers.FireResetEnv(env)
# This wrapper transforms rewards to {-1, 0, 1} according to their sign
if clip_rewards:
env = atari_wrappers.ClipRewardEnv(env)
# This wrapper is yours :)
env = PreprocessAtariObs(env)
return env
def PrimaryAtariWrap(env,
clip_rewards=True,
frame_skip=True,
fire_reset_event=False,
episodic_life=False,
width=44,
height=44,
margins=[1, 1, 1, 1],
n_frames=4,
reward_scale=0):
# This wrapper holds the same action for <skip> frames and outputs
# the maximal pixel value of 2 last frames (to handle blinking
# in some envs)
if frame_skip:
env = atari_wrappers.MaxAndSkipEnv(env, skip=4)
# This wrapper sends done=True when each life is lost
# (not all the 5 lives that are givern by the game rules).
# It should make easier for the agent to understand that losing is bad.
if episodic_life:
env = atari_wrappers.EpisodicLifeEnv(env)
# This wrapper laucnhes the ball when an episode starts.
# Without it the agent has to learn this action, too.
# Actually it can but learning would take longer.
if fire_reset_event:
env = atari_wrappers.FireResetEnv(env)
# This wrapper transforms rewards to {-1, 0, 1} according to their sign
if clip_rewards:
env = atari_wrappers.ClipRewardEnv(env)
if reward_scale != 0:
env = atari_wrappers.RewardScale(env, reward_scale)
# This wrapper is yours :)
env = atari_wrappers.PreprocessAtariObs(env,
height=height,
width=width,
margins=margins)
env = atari_wrappers.FrameBuffer(env,
n_frames=n_frames,
dim_order='pytorch')
return env
args = parser.parse_args()
ENVIRONMENT = args.env
MODEL_FOLDER = args.model_folder
MODEL_NAME = args.model_name
CHECKPOINT = args.checkpoint
TRAINING_INFO = args.training_info
NETWORK = args.network
CHECKPOINT_STEPS = args.checkpoint_steps
additional_training_steps = args.add_training_steps
########## LOAD ENVIRONMENT AND BUILD NETWORK ##########
env = gym.make(ENVIRONMENT)
env = atari_wrappers.wrap_deepmind(env, frame_stack=True, clip_rewards=True)
env = atari_wrappers.MaxAndSkipEnv(env, skip=3)
#env = atari_wrappers.CenteredScaledFloatFrame(env)
optimizer = tf.train.RMSPropOptimizer(learning_rate=0.00025, momentum=0.95, epsilon=0.01)
#optimizer = tf.train.AdamOptimizer(learning_rate=1e-4)
use_target_network = True #False if NETWORK.startswith('double') else True
use_double_dqn = True if NETWORK.startswith('double') else False
net = dqn.get_network(type=NETWORK, input_shape=env.observation_space.shape, num_actions=env.action_space.n,
use_target_network=use_target_network, use_double_dqn=use_double_dqn, optimizer=optimizer)
#################### TRAINING AGENT ####################
saver = tf.train.Saver(max_to_keep=10)