def inference(args):
indir = os.path.join(args.log_dir, 'train')
outdir = os.path.join(args.log_dir, 'player') if args.out_dir is None else args.out_dir
with open(indir + "/checkpoint", "r") as f:
first_line = f.readline().strip()
print ("first_line is : {}".format(first_line))
ckpt = first_line.split(' ')[-1].split('/')[-1][:-1]
ckpt = ckpt.split('-')[-1]
ckpt = indir + '/model.ckpt-' + ckpt
print ("ckpt: {}".format(ckpt))
# define environment
env = create_icegame_env(outdir, args.env_id)
num_actions = env.action_space.n
with tf.device("/cpu:0"):
# define policy network
with tf.variable_scope("global"):
policy = LSTMPolicy(env.observation_space.shape, num_actions)
policy.global_step = tf.get_variable("global_step", [],
tf.int32, initializer=tf.constant_initializer(0, dtype=tf.int32), trainable=False)
# Variable names that start with "local" are not saved in checkpoints.
variables_to_restore = [v for v in tf.global_variables() if not v.name.startswith("local")]
init_all_op = tf.global_variables_initializer()
saver = FastSaver(variables_to_restore)
# print trainable variables
var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, tf.get_variable_scope().name)
logger.info('Trainable vars:')
for v in var_list:
logger.info(' {} {}'.format(v.name, v.get_shape()))
logger.info("Restored the trained model.")
# summary of rewards
action_writers = []
summary_writer = tf.summary.FileWriter(outdir)
for act_idx in range(num_actions):
action_writers.append(tf.summary.FileWriter(
os.path.join(outdir, "action_{}".format(act_idx))
))
logger.info("Inference events directory: %s", outdir)
config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)
with tf.Session() as sess:
logger.info("Initializing all parameters.")
sess.run(init_all_op)
logger.info("Restoring trainable global parameters.")
saver.restore(sess, ckpt)
logger.info("Restored model was trained for %.2fM global steps", sess.run(policy.global_step)/1000000.)
last_features = policy.get_initial_features() # reset lstm memory
length = 0
rewards = 0
# For plotting
plt.ion()
fig = plt.figure(num=None, figsize=(8, 8), dpi=92, facecolor='w', edgecolor='k')
gs1 = gridspec.GridSpec(3, 3)
gs1.update(left=0.05, right=0.85, wspace=0.15)
ax1 = plt.subplot(gs1[:-1, :])
ax2 = plt.subplot(gs1[-1, :-1])
ax3 = plt.subplot(gs1[-1, -1])
ax1.set_title("IceGame (Agent Lives: {}, UpTimes: {})".format(env.lives, env.sim.get_updated_counter()))
ind = np.arange(num_actions)
width = 0.20
#action_legends = ["Up", "Down", "Left", "Right", "NextUp", "NextDown", "Metropolis"]
action_legends = [">", "v", "<", "^", "", "", "Metro"]
for ep in range(args.num_episodes):
"""TODO: policy sampling strategy
random, greedy and sampled policy.
"""
last_state = env.reset()
steps_rewards=[]
steps_values=[]
# running policy
while True:
fetched = policy.act_inference(last_state, *last_features)
prob_action, action, value_, features = fetched[0], fetched[1], fetched[2], fetched[3:]
#TODO: policy sampling strategy
# Greedy
#print ("Prob of actions: {}".format(prob_action))
stepAct = action.argmax()
state, reward, terminal, info = env.step(stepAct)
# update stats
length += 1
rewards += reward
last_state = state
last_features = features
steps_rewards.append(rewards)
steps_values.append(value_)
if info:
loopsize = info["Loop Size"]
looparea = info["Loop Area"]
"""Animation for State and Actions
"""
ax2.clear()
ax2.bar(ind, prob_action)
ax2.set_xticks(ind + width / 2)
ax2.set_xticklabels(action_legends)
ax1.imshow(state[:,:,2], 'Reds', interpolation="None", vmin=-1, vmax=1)
# with hist
#ax1.imshow(state[:,:,7], 'Reds', interpolation="None", vmin=-1, vmax=1)
ax1.set_title("IceGame: (Agent Lives: {}, UpTimes: {})".format(env.lives, env.sim.get_updated_counter()))
ax3.clear()
ax3.plot(steps_rewards, linewidth=2)
ax3.plot(steps_values, linewidth=2)
#plt.savefig("records/{}.png".format(length))
plt.pause(0.20)
# store summary
summary = tf.Summary()
summary.value.add(tag='ep_{}/reward'.format(ep), simple_value=reward)
summary.value.add(tag='ep_{}/netreward'.format(ep), simple_value=rewards)
summary.value.add(tag='ep_{}/value'.format(ep), simple_value=float(value_[0]))
if info:
summary.value.add(tag='ep_{}/loop_size'.format(ep), simple_value=loopsize)
summary.value.add(tag='ep_{}/loop_area'.format(ep), simple_value=looparea)
summary_writer.add_summary(summary, length)
summary_writer.flush()
summary = tf.Summary()
for ac_id in range(num_actions):
summary.value.add(tag='ep_{}/a_{}'.format(ep, ac_id), simple_value=float(prob_action[ac_id]))
action_writers[ac_id].add_summary(summary, length)
action_writers[ac_id].flush()
"""TODO:
1. Need more concrete idea for playing the game when interfering.
2. Save these values for post processing.
"""
if terminal:
#if length >= timestep_limit:
# last_state, _, _, _ = env.reset()
last_features = policy.get_initial_features() # reset lstm memory
print("Episode finished. Sum of rewards: %.2f. Length: %d." % (rewards, length))
length = 0
rewards = 0
break
logger.info('Finished %d true episodes.', args.num_episodes)
plt.savefig("GameScene.png")
logger.info("Save the last scene to GameScene.png")
env.close()
def inference(args):
"""
It only restores LSTMPolicy architecture, and does inference using that.
"""
# get address of checkpoints
indir = os.path.join(args.log_dir, 'train')
outdir = os.path.join(
args.log_dir, 'inference') if args.out_dir is None else args.out_dir
with open(indir + '/checkpoint', 'r') as f:
first_line = f.readline().strip()
ckpt = first_line.split(' ')[-1].split('/')[-1][:-1]
ckpt = ckpt.split('-')[-1]
ckpt = indir + '/model.ckpt-' + ckpt
# define environment
if args.record:
env = create_env(args.env_id,
client_id='0',
remotes=None,
envWrap=args.envWrap,
designHead=args.designHead,
record=True,
noop=args.noop,
acRepeat=args.acRepeat,
outdir=outdir)
else:
env = create_env(args.env_id,
client_id='0',
remotes=None,
envWrap=args.envWrap,
designHead=args.designHead,
record=True,
noop=args.noop,
acRepeat=args.acRepeat)
numaction = env.action_space.n
with tf.device("/cpu:0"):
# define policy network
with tf.variable_scope("global"):
policy = LSTMPolicy(env.observation_space.shape, numaction,
args.designHead)
policy.global_step = tf.get_variable(
"global_step", [],
tf.int32,
initializer=tf.constant_initializer(0, dtype=tf.int32),
trainable=False)
# Variable names that start with "local" are not saved in checkpoints.
if use_tf12_api:
variables_to_restore = [
v for v in tf.global_variables()
if not v.name.startswith("local")
]
init_all_op = tf.global_variables_initializer()
else:
variables_to_restore = [
v for v in tf.all_variables() if not v.name.startswith("local")
]
init_all_op = tf.initialize_all_variables()
saver = FastSaver(variables_to_restore)
# print trainable variables
var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
tf.get_variable_scope().name)
logger.info('Trainable vars:')
for v in var_list:
logger.info(' %s %s', v.name, v.get_shape())
# summary of rewards
action_writers = []
if use_tf12_api:
summary_writer = tf.summary.FileWriter(outdir)
for ac_id in range(numaction):
action_writers.append(
tf.summary.FileWriter(
os.path.join(outdir, 'action_{}'.format(ac_id))))
else:
summary_writer = tf.train.SummaryWriter(outdir)
for ac_id in range(numaction):
action_writers.append(
tf.train.SummaryWriter(
os.path.join(outdir, 'action_{}'.format(ac_id))))
logger.info("Inference events directory: %s", outdir)
config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
with tf.Session(config=config) as sess:
logger.info("Initializing all parameters.")
sess.run(init_all_op)
logger.info("Restoring trainable global parameters.")
saver.restore(sess, ckpt)
logger.info("Restored model was trained for %.2fM global steps",
sess.run(policy.global_step) / 1000000.)
#saving with meta graph:
metaSaver = tf.train.Saver(variables_to_restore)
metaSaver.save(
sess, '/home/swagking0/noreward-rl/models/models_me/mario_me')
last_state = env.reset()
if args.render or args.record:
env.render()
last_features = policy.get_initial_features() # reset lstm memory
length = 0
rewards = 0
mario_distances = np.zeros((args.num_episodes, ))
for i in range(args.num_episodes):
print("Starting episode %d" % (i + 1))
if args.recordSignal:
from PIL import Image
signalCount = 1
utils.mkdir_p(outdir + '/recordedSignal/ep_%02d/' % i)
Image.fromarray(
(255 * last_state[..., -1]).astype('uint8')).save(
outdir + '/recordedSignal/ep_%02d/%06d.jpg' %
(i, signalCount))
if args.random:
print('I am random policy!')
else:
if args.greedy:
print('I am greedy policy!')
else:
print('I am sampled policy!')
while True:
# run policy
fetched = policy.act_inference(last_state, *last_features)
prob_action, action, value_, features = fetched[
0], fetched[1], fetched[2], fetched[3:]
# run environment: sampled one-hot 'action' (not greedy)
if args.random:
stepAct = np.random.randint(0,
numaction) # random policy
else:
if args.greedy:
stepAct = prob_action.argmax() # greedy policy
else:
stepAct = action.argmax()
# print(stepAct, prob_action.argmax(), prob_action)
state, reward, terminal, info = env.step(stepAct)
# update stats
length += 1
rewards += reward
last_state = state
last_features = features
if args.render or args.record:
env.render()
if args.recordSignal:
signalCount += 1
Image.fromarray(
(255 * last_state[..., -1]).astype('uint8')).save(
outdir + '/recordedSignal/ep_%02d/%06d.jpg' %
(i, signalCount))
# store summary
summary = tf.Summary()
summary.value.add(tag='ep_{}/reward'.format(i),
simple_value=reward)
summary.value.add(tag='ep_{}/netreward'.format(i),
simple_value=rewards)
summary.value.add(tag='ep_{}/value'.format(i),
simple_value=float(value_[0]))
if 'NoFrameskip-v' in args.env_id: # atari
summary.value.add(
tag='ep_{}/lives'.format(i),
simple_value=env.unwrapped.ale.lives())
summary_writer.add_summary(summary, length)
summary_writer.flush()
summary = tf.Summary()
for ac_id in range(numaction):
summary.value.add(tag='action_prob',
simple_value=float(
prob_action[ac_id]))
action_writers[ac_id].add_summary(summary, length)
action_writers[ac_id].flush()
timestep_limit = env.spec.tags.get(
'wrapper_config.TimeLimit.max_episode_steps')
if timestep_limit is None:
timestep_limit = env.spec.timestep_limit
if terminal or length >= timestep_limit:
if length >= timestep_limit or not env.metadata.get(
'semantics.autoreset'):
last_state = env.reset()
last_features = policy.get_initial_features(
) # reset lstm memory
print(
"Episode finished. Sum of rewards: %.2f. Length: %d."
% (rewards, length))
if 'distance' in info:
print('Mario Distance Covered:', info['distance'])
mario_distances[i] = info['distance']
length = 0
rewards = 0
if args.render or args.record:
env.render()
if args.recordSignal:
signalCount += 1
Image.fromarray(
(255 *
last_state[..., -1]).astype('uint8')).save(
outdir +
'/recordedSignal/ep_%02d/%06d.jpg' %
(i, signalCount))
break
logger.info('Finished %d true episodes.', args.num_episodes)
if 'distance' in info:
print('Mario Distances:', mario_distances)
np.save(outdir + '/distances.npy', mario_distances)
env.close()
def inference(args):
indir = os.path.join(args.log_dir, 'train')
outdir = os.path.join(
args.log_dir, 'inference') if args.out_dir is None else args.out_dir
with open(indir + "/checkpoint", "r") as f:
first_line = f.readline().strip()
print("first_line is : {}".format(first_line))
ckpt = first_line.split(' ')[-1].split('/')[-1][:-1]
ckpt = ckpt.split('-')[-1]
ckpt = indir + '/model.ckpt-' + ckpt
print("ckpt: {}".format(ckpt))
# define environment
env = create_icegame_env(outdir, args.env_id)
num_actions = env.action_space.n
with tf.device("/cpu:0"):
# define policy network
with tf.variable_scope("global"):
policy = LSTMPolicy(env.observation_space.shape, num_actions)
policy.global_step = tf.get_variable(
"global_step", [],
tf.int32,
initializer=tf.constant_initializer(0, dtype=tf.int32),
trainable=False)
# Variable names that start with "local" are not saved in checkpoints.
variables_to_restore = [
v for v in tf.global_variables() if not v.name.startswith("local")
]
init_all_op = tf.global_variables_initializer()
saver = FastSaver(variables_to_restore)
# print trainable variables
var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
tf.get_variable_scope().name)
logger.info('Trainable vars:')
for v in var_list:
logger.info(' {} {}'.format(v.name, v.get_shape()))
logger.info("Restored the trained model.")
# summary of rewards
action_writers = []
summary_writer = tf.summary.FileWriter(outdir)
for act_idx in range(num_actions):
action_writers.append(
tf.summary.FileWriter(
os.path.join(outdir, "action_{}".format(act_idx))))
logger.info("Inference events directory: %s", outdir)
config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
with tf.Session() as sess:
logger.info("Initializing all parameters.")
sess.run(init_all_op)
logger.info("Restoring trainable global parameters.")
saver.restore(sess, ckpt)
logger.info("Restored model was trained for %.2fM global steps",
sess.run(policy.global_step) / 1000000.)
last_features = policy.get_initial_features() # reset lstm memory
length = 0
rewards = 0
loopsizes = []
# All Episodes records
for ep in range(args.num_episodes):
"""TODO: policy sampling strategy
random, greedy and sampled policy.
"""
last_state = env.reset()
# Episode records
# running policy
while True:
fetched = policy.act_inference(last_state, *last_features)
prob_action, action, value_, features = fetched[
0], fetched[1], fetched[2], fetched[3:]
#TODO: policy sampling strategy
# Greedy
stepAct = action.argmax()
state, reward, terminal, info = env.step(stepAct)
# update stats
length += 1
rewards += reward
last_state = state
last_features = features
"""TODO: Resonable Statistics are necessary
"""
if info:
loopsize = info["Loop Size"]
looparea = info["Loop Area"]
# store summary
summary = tf.Summary()
summary.value.add(tag='ep_{}/reward'.format(ep),
simple_value=reward)
summary.value.add(tag='ep_{}/netreward'.format(ep),
simple_value=rewards)
summary.value.add(tag='ep_{}/value'.format(ep),
simple_value=float(value_[0]))
if info:
summary.value.add(tag='ep_{}/loop_size'.format(ep),
simple_value=loopsize)
summary.value.add(tag='ep_{}/loop_area'.format(ep),
simple_value=looparea)
loopsizes.append(loopsize)
summary_writer.add_summary(summary, length)
summary_writer.flush()
summary = tf.Summary()
for ac_id in range(num_actions):
summary.value.add(tag='ep_{}/a_{}'.format(ep, ac_id),
simple_value=float(
prob_action[ac_id]))
action_writers[ac_id].add_summary(summary, length)
action_writers[ac_id].flush()
"""TODO:
1. Need more concrete idea for playing the game when interfering.
2. Save these values for post processing.
"""
if terminal:
#if length >= timestep_limit:
# last_state, _, _, _ = env.reset()
last_features = policy.get_initial_features(
) # reset lstm memory
print(
"Episode finished. Sum of rewards: %.2f. Length: %d."
% (rewards, length))
length = 0
rewards = 0
break
logger.info('Finished %d true episodes.', args.num_episodes)
# Count loop topology
unique, counts = np.unique(loopsizes, return_counts=True)
loopstatistics = dict(zip(unique, counts))
print(loopstatistics)
env.close()