def __init__(self, arglist):
self.env_name = arglist.game
self.vae = ConvVAE(batch_size=1,
gpu_mode=False,
is_training=False,
reuse=True)
self.vae.load_json(arglist.vae_file)
self.rnn = MDNRNN(hps_sample, gpu_mode=False, reuse=True)
self.rnn.load_json(arglist.rnn_file)
self.state = rnn_init_state(self.rnn)
self.rnn_mode = True
self.input_size = rnn_output_size(EXP_MODE)
self.z_size = 32
if EXP_MODE == MODE_Z_HIDDEN: # one hidden layer
self.hidden_size = 40
self.weight_hidden = np.random.randn(self.input_size,
self.hidden_size)
self.bias_hidden = np.random.randn(self.hidden_size)
self.weight_output = np.random.randn(self.hidden_size, 2)
self.bias_output = np.random.randn(2)
self.param_count = ((self.input_size + 1) *
self.hidden_size) + (self.hidden_size * 2 + 2)
else:
self.weight = np.random.randn(self.input_size, 2)
self.bias = np.random.randn(2)
self.param_count = (self.input_size) * 2 + 2
self.render_mode = False
def __init__(self, load_model=True):
self.env_name = './VisualPushBlock_withBlock_z_info.x86_64' #'./VisualPushBlock.x86_64'
self.vae = ConvVAE(batch_size=1,
gpu_mode=False,
is_training=False,
reuse=True)
self.rnn = MDNRNN(hps_sample, gpu_mode=False, reuse=True)
if load_model:
self.vae.load_json('vae/vae.json')
self.rnn.load_json('rnn/rnn.json')
self.state = rnn_init_state(self.rnn)
self.rnn_mode = True
self.input_size = rnn_output_size(EXP_MODE)
self.z_size = z_size
if EXP_MODE == MODE_Z_HIDDEN: # one hidden layer ###CHANGE is made here
self.hidden_size = 40
self.weight_hidden = np.random.randn(self.input_size,
self.hidden_size)
self.bias_hidden = np.random.randn(self.hidden_size)
self.weight_output = np.random.randn(self.hidden_size, ACTION_SIZE)
self.bias_output = np.random.randn(ACTION_SIZE)
self.param_count = ((self.input_size + 1) * self.hidden_size) + (
self.hidden_size * ACTION_SIZE + ACTION_SIZE)
else:
self.weight = np.random.randn(self.input_size, ACTION_SIZE)
self.bias = np.random.randn(ACTION_SIZE)
self.param_count = (self.input_size) * ACTION_SIZE + ACTION_SIZE
self.render_mode = False
def __init__(self):
self.env_name = "carracing"
self.vae = ConvVAE(batch_size=1, gpu_mode=False, is_training=False, reuse=True)
self.vae.load_json('vae/vae.json')
self.rnn = MDNRNN(hps_sample, gpu_mode=False, reuse=True)
self.rnn.load_json('rnn/rnn.json')
self.state = rnn_init_state(self.rnn)
self.rnn_mode = True
self.input_size = rnn_output_size(EXP_MODE)
self.z_size = 32
if EXP_MODE == MODE_Z_HIDDEN: # one hidden layer
self.hidden_size = 40
self.weight_hidden = np.random.randn(self.input_size, self.hidden_size)
self.bias_hidden = np.random.randn(self.hidden_size)
self.weight_output = np.random.randn(self.hidden_size, 3)
self.bias_output = np.random.randn(3)
self.param_count = ((self.input_size+1)*self.hidden_size) + (self.hidden_size*3+3)
else:
self.weight = np.random.randn(self.input_size, 3)
self.bias = np.random.randn(3)
self.param_count = (self.input_size)*3+3
self.render_mode = False
def __init__(self, load_model=True):
# For Mac
# self.env_name = "/Users/intuinno/codegit/pushBlock/app/mac/VisualPushBlockContinuous"
# For linux
self.env_name = "/home/intuinno/codegit/pushblock/app/linux/pushblock.x86_64"
self.vae = ConvVAE(batch_size=1, gpu_mode=False, is_training=False, reuse=True)
self.rnn = MDNRNN(hps_sample, gpu_mode=False, reuse=True)
if load_model:
self.vae.load_json('vae/vae.json')
self.rnn.load_json('rnn/rnn.json')
self.state = rnn_init_state(self.rnn)
self.rnn_mode = True
self.input_size = rnn_output_size(EXP_MODE)
self.z_size = 32
if EXP_MODE == MODE_Z_HIDDEN: # one hidden layer
self.hidden_size = 40
self.weight_hidden = np.random.randn(self.input_size, self.hidden_size)
self.bias_hidden = np.random.randn(self.hidden_size)
self.weight_output = np.random.randn(self.hidden_size, 3)
self.bias_output = np.random.randn(3)
self.param_count = ((self.input_size+1)*self.hidden_size) + (self.hidden_size*3+3)
else:
self.weight = np.random.randn(self.input_size, 3)
self.bias = np.random.randn(3)
self.param_count = (self.input_size)*3+3
self.render_mode = False
def __init__(self,
args,
load_model=True,
full_episode=False,
with_obs=False):
super(CarRacingMDNRNN, self).__init__(full_episode=full_episode)
self.with_obs = with_obs # whether or not to return the frame with the encodings
self.vae = CVAE(args)
self.rnn = MDNRNN(args)
if load_model:
self.vae.set_weights(
tf.keras.models.load_model('results/{}/{}/tf_vae'.format(
args.exp_name, args.env_name),
compile=False).get_weights())
self.rnn.set_weights(
tf.keras.models.load_model('results/{}/{}/tf_rnn'.format(
args.exp_name, args.env_name),
compile=False).get_weights())
self.rnn_states = rnn_init_state(self.rnn)
self.full_episode = False
self.observation_space = Box(low=np.NINF,
high=np.Inf,
shape=(32 + 256))
def __init__(self,
args,
load_model=True,
full_episode=False,
with_obs=False):
super(CarRacingMDNRNN, self).__init__(full_episode=full_episode)
self.with_obs = with_obs # whether or not to return the frame with the encodings
self.vae = CVAE(args)
self.rnn = MDNRNN(args)
if load_model:
self.vae.set_weights([
param_i.numpy() for param_i in tf.saved_model.load(
'results/{}/tf_vae'.format(args.env_name)).variables
])
self.rnn.set_weights([
param_i.numpy() for param_i in tf.saved_model.load(
'results/{}/tf_rnn'.format(args.env_name)).variables
])
self.rnn_states = rnn_init_state(self.rnn)
self.full_episode = False
self.observation_space = Box(low=np.NINF,
high=np.Inf,
shape=(args.z_size +
args.rnn_size * args.state_space))
def reset(self, test=False):
self.state_rnn = rnn_init_state(self.rnn)
if self.seed:
self.env.seed(random.choice(self.seed))
self.flip_episode = random.random() > 0.5 and not test and self.flip
state, self.state_rnn = self.encode_obs(self.env.reset(), self.state_rnn, np.array([0.5, 0.2, 0.8]))
return state, 1
def __init__(self, arglist, action_space, scope, load_model=True):
self.action_space = action_space
self.arglist = arglist
self.vae = ConvVAE(batch_size=1,
gpu_mode=False,
is_training=False,
reuse=True)
hps_sample = hps_model._replace(
batch_size=1,
input_seq_width=32 + arglist.action_space +
(arglist.agent_num - 1) * arglist.action_space * arglist.timestep,
max_seq_len=1,
use_recurrent_dropout=0,
is_training=0)
self.rnn = MDNRNN(hps_sample, gpu_mode=False, reuse=True)
if load_model:
self.vae.load_json(arglist.vae_model_dir)
self.rnn.load_json(arglist.rnn_model_dir)
self.state = rnn_init_state(self.rnn)
self.rnn_mode = True
if arglist.inference:
self.input_size = rnn_output_size(
EXP_MODE) + (arglist.agent_num - 1) * arglist.action_space
else:
self.input_size = rnn_output_size(EXP_MODE)
self.z_size = 32
# action trajectories recording
self.act_traj = [
collections.deque(np.zeros(
(arglist.timestep, arglist.action_space)),
maxlen=arglist.timestep)
] * (arglist.agent_num - 1)
self.oppo_model = Oppo_Model(arglist.agent_num, arglist.timestep,
arglist.action_space,
arglist.action_space,
"oppo_model_{}".format(scope))
self.inference = arglist.inference
if EXP_MODE == MODE_Z_HIDDEN: # one hidden layer
self.hidden_size = 40
self.weight_hidden = np.random.randn(self.input_size,
self.hidden_size)
self.bias_hidden = np.random.randn(self.hidden_size)
self.weight_output = np.random.randn(self.hidden_size,
self.action_space)
self.bias_output = np.random.randn(self.action_space)
self.param_count = ((self.input_size + 1) * self.hidden_size) + (
self.hidden_size * self.action_space + self.action_space)
else:
self.weight = np.random.randn(self.input_size, self.action_space)
self.bias = np.random.randn(self.action_space)
self.param_count = (
self.input_size) * self.action_space + self.action_space
def reset(self):
self.rnn_states = rnn_init_state(self.rnn)
if self.with_obs:
[z_state, obs] = super(CarRacingMDNRNN, self).reset() # calls step
self.N_tiles = len(self.track)
return [z_state, obs]
else:
z_state = super(CarRacingMDNRNN, self).reset() # calls step
self.N_tiles = len(self.track)
return z_state
def _reset(self):
obs = super(DoomTakeCoverMDNRNN, self)._reset()
small_obs = self._process_frame(obs)
self.current_obs = small_obs
self.rnn_states = rnn_init_state(self.rnn)
self.z = self._encode(small_obs)
self.restart = 1
self.frame_count = 0
if self.with_obs:
return [self._current_state(), self.current_obs]
else:
return self._current_state()
def reset(self):
self.rnn_states = rnn_init_state(self.rnn)
obs = super(CarRacingMDNRNN, self).reset()
obs = self._process_frame(obs)
z = self.encode_obs(obs)
h = tf.squeeze(self.rnn_states[0])
z_h = tf.concat([z, h], axis=-1)
if self.with_obs:
return [z_h, obs]
else:
z_h = super(CarRacingMDNRNN, self).reset() # calls step
return z_h
def __init__(self, load_model=True, full_episode=False):
super(CarRacingMDNRNN, self).__init__(full_episode=full_episode)
self.vae = CVAE(batch_size=1)
self.rnn = MDNRNN(hps_sample)
if load_model:
self.vae.load_json('tf_vae/vae.json')
self.rnn.load_json('tf_rnn/rnn.json')
self.rnn_states = rnn_init_state(self.rnn)
self.full_episode = False
self.observation_space = Box(low=np.NINF, high=np.Inf, shape=(32+256))
def reset(self, **kwargs):
# TODO: Is this zeroes? Can this just be None?
self.rnn_state = rnn_init_state(self.rnn) # [h, c]
# reset() of wrapped environment might call step() and already set self.z in the process
self.z = None
obs = self.env.reset(**kwargs)
# If step() was not called, calculate z from initial observation
if self.z is None:
self.z = self.encode_image_to_z(obs)
obs = self.modify_observation(obs)
return obs
def __init__(self,
index=0,
rl_training=False,
restore_model=False,
global_buffer=None,
net=None,
strategy_agent=None,
greedy_action=False,
extract_save_dir=None,
load_model=True,
ntype='worldmodel'):
super(MiniSourceAgent, self).__init__()
self.net = net
self.index = index
self.global_buffer = global_buffer
self.restore_model = restore_model
# model in brain
self.strategy_agent = strategy_agent
self.strategy_act = None
# count num
self.step = 0
self.strategy_wait_secs = 2
self.strategy_flag = False
self.policy_wait_secs = 2
self.policy_flag = True
self.env = None
self.obs = None
# buffer
self.local_buffer = Buffer()
self.num_players = 2
self.on_select = None
self._result = None
self._gases = None
self.is_end = False
self.greedy_action = greedy_action
self.rl_training = rl_training
self.extract_save_dir = extract_save_dir
self.rnn_state = rnn_init_state(self.net.rnn)
def reset(self):
super(MiniSourceAgent, self).reset()
self.step = 0
self.obs = None
self._result = None
self._gases = None
self.is_end = False
self.strategy_flag = False
self.policy_flag = True
self.local_buffer.reset()
if self.strategy_agent is not None:
self.strategy_agent.reset()
self.rnn_state = rnn_init_state(self.net.rnn)
def __init__(self,
model_name='',
load_model=True,
load_full_model=False,
full_model_path=''):
self.model_name = model_name
self.env_name = "carracing"
self.vae = ConvVAE(batch_size=1,
gpu_mode=False,
is_training=False,
reuse=True)
self.rnn = MDNRNN(hps_sample, gpu_mode=False, reuse=True)
if load_full_model:
self.vae.load_json(os.path.join(full_model_path, 'vae.json'))
self.rnn.load_json(os.path.join(full_model_path, 'rnn.json'))
elif load_model:
self.vae.load_json(
os.path.join(vae_path, self.model_name + '_vae.json'))
self.rnn.load_json(
os.path.join(rnn_path, self.model_name + '_rnn.json'))
self.state = rnn_init_state(self.rnn)
self.rnn_mode = True
self.input_size = rnn_output_size(EXP_MODE)
self.z_size = 32
if EXP_MODE == MODE_Z_HIDDEN: # one hidden layer
self.hidden_size = 40
self.weight_hidden = np.random.randn(self.input_size,
self.hidden_size)
self.bias_hidden = np.random.randn(self.hidden_size)
self.weight_output = np.random.randn(self.hidden_size, 3)
self.bias_output = np.random.randn(3)
self.param_count = ((self.input_size + 1) *
self.hidden_size) + (self.hidden_size * 3 + 3)
else:
self.weight = np.random.randn(self.input_size, 3)
self.bias = np.random.randn(3)
self.param_count = (self.input_size) * 3 + 3
self.render_mode = False
def __init__(self, load_model=True):
self.env_name = "Pong"
self._make_env()
self.vae = ConvVAE(batch_size=1,
gpu_mode=False,
is_training=False,
reuse=True)
hps_sample_dynamic = hps_sample._replace(num_actions=self.num_actions)
self.rnn = MDNRNN(hps_sample_dynamic, gpu_mode=False, reuse=True)
if load_model:
self.vae.load_json('vae/vae.json')
self.rnn.load_json('rnn/rnn.json')
self.state = rnn_init_state(self.rnn)
self.rnn_mode = True
self.input_size = rnn_output_size(EXP_MODE)
self.z_size = 32
if EXP_MODE == MODE_Z_HIDDEN: # one hidden layer
raise Exception("not ported for atari")
self.hidden_size = 40
self.weight_hidden = np.random.randn(self.input_size,
self.hidden_size)
self.bias_hidden = np.random.randn(self.hidden_size)
self.weight_output = np.random.randn(self.hidden_size,
self.num_actions)
self.bias_output = np.random.randn(self.num_actions)
self.param_count = ((self.input_size + 1) * self.hidden_size) + (
(self.hidden_size + 1) * self.num_actions)
else:
# TODO: Not known until env.action_space is queried...
self.weight = np.random.randn(self.input_size, self.num_actions)
self.bias = np.random.randn(self.num_actions)
self.param_count = (self.input_size + 1) * self.num_actions
self.render_mode = False
def __init__(self, load_model=True, env_name="Pong-v0", render_mode=False):
self.env_name = env_name
self.make_env()
self.z_size = 32
self.vae = ConvVAE(batch_size=1,
gpu_mode=False,
is_training=False,
reuse=True)
hps_atari = hps_sample._replace(input_seq_width=self.z_size + self.na)
self.rnn = MDNRNN(hps_atari, gpu_mode=False, reuse=True)
if load_model:
self.vae.load_json('vae/vae.json')
self.rnn.load_json('rnn/rnn.json')
self.state = rnn_init_state(self.rnn)
self.rnn_mode = True
self.input_size = rnn_output_size(EXP_MODE)
self.init_controller()
self.render_mode = False
def reset(self):
self.rnn_state = rnn_init_state(self.rnn)
self.z = self._sample_init_z()
obs = OrderedDict(features=rnn_output(self.rnn_state, self.z, self.features_mode))
return obs
def reset(self): self.rnn_states = rnn_init_state(self.rnn) z_h = super(CarRacingWrapper, self).reset() # calls step return z_h
def reset(self):
self.rnn_states = rnn_init_state(self.rnn)
z = np.expand_dims(self._sample_init_z(), axis=0)
self.o = z
z_ch = tf.concat([z, self.rnn_states[1], self.rnn_states[0]], axis=-1)
return tf.squeeze(z_ch)
def train(sess, env, actor, critic, global_step):
# Set up summary Ops
summary_ops, summary_vars = build_summaries()
sess.run(tf.global_variables_initializer())
# load model if have
saver = tf.train.Saver()
checkpoint = tf.train.get_checkpoint_state("./results")
if checkpoint and checkpoint.model_checkpoint_path:
saver.restore(sess, checkpoint.model_checkpoint_path)
print("Successfully loaded:", checkpoint.model_checkpoint_path)
print("global step: ", global_step.eval())
else:
print("Could not find old network weights")
writer = tf.summary.FileWriter(SUMMARY_DIR, sess.graph)
# Initialize target network weights
actor.update_target_network()
critic.update_target_network()
# Initialize replay memory
replay_buffer = ReplayBuffer(BUFFER_SIZE, RANDOM_SEED)
state = rnn_init_state(rnn)
i = global_step.eval()
eps = 1
lr = INITIAL_LR
while True:
i += 1
s = env.reset()
s, state = encode_obs(s, state, np.array([0.5, 0.2, 0.8]))
# s = prepro(s)
ep_reward = 0
ep_ave_max_q = 0
eps *= EPS_DECAY_RATE
lr *= LR_DECAY_RATE
lr = np.max([lr, MINI_LR]) # minimum of learning rate is MINI_LR
if i % SAVE_STEP == 0: # save check point every 1000 episode
sess.run(global_step.assign(i))
save_path = saver.save(sess,
"./results/model.ckpt",
global_step=global_step)
print("Model saved in file: %s" % save_path)
print("Successfully saved global step: ", global_step.eval())
for j in range(MAX_EP_STEPS):
if RENDER_ENV:
env.render()
# print(s.shape)
a = actor.predict(np.reshape(s, (-1, 16, 10, 2)))
print(a)
# action = a[0] + 1./(1+i+j) # add noise for exploration
noise = np.random.normal(0, 0.2 * eps, 3)
noise[1] = np.random.normal(0.4, 0.1 * eps)
action = a[0] + noise
s2, r, terminal, info = env.step(action)
s2, state = encode_obs(s2, state, action)
# s2 = prepro(s2)
action = np.expand_dims(action, axis=0)
# plt.imshow(s2)
# plt.show()
# if r > 0:
# r = 1
# elif r < 0:
# r = -1
# print 'r: ',r
# replay_buffer.add(np.reshape(s, (96, 96, 3)), np.reshape(action, (actor.a_dim,)), r,
# terminal, np.reshape(s2, (96, 96, 3)),lr)
replay_buffer.add(s, np.reshape(action, (actor.a_dim, )), r,
terminal, s2, lr)
# Keep adding experience to the memory until
# there are at least minibatch size samples
if replay_buffer.size() > MINIBATCH_SIZE:
s_batch, a_batch, r_batch, t_batch, s2_batch, lr_batch = \
replay_buffer.sample_batch(MINIBATCH_SIZE)
# Calculate targets
s2_batch = np.reshape(s2_batch, (64, 16, 10, 2))
target_q = critic.predict_target(
s2_batch, actor.predict_target(s2_batch))
y_i = []
for k in range(MINIBATCH_SIZE):
if t_batch[k]:
y_i.append(r_batch[k])
else:
y_i.append(r_batch[k] + GAMMA * target_q[k])
# Update the critic given the targets
s_batch = np.reshape(s_batch, (64, 16, 10, 2))
predicted_q_value, _ = critic.train(
s_batch, a_batch, np.reshape(y_i, (MINIBATCH_SIZE, 1)),
lr_batch)
ep_ave_max_q += np.amax(predicted_q_value)
# print ep_ave_max_q
# Update the actor policy using the sampled gradient
a_outs = actor.predict(s_batch)
grads = critic.action_gradients(s_batch, a_outs)
# print grads[0]
actor.train(s_batch, grads[0], lr_batch)
# Update target networks
actor.update_target_network()
critic.update_target_network()
summary_str = sess.run(summary_ops,
feed_dict={
summary_vars[0]: ep_reward,
summary_vars[1]:
ep_ave_max_q / float(j)
})
writer.add_summary(summary_str, i)
writer.flush()
print('| Reward : %.2i' % int(ep_reward), " | Episode", i, \
'| Qmax: %.4f' % (ep_ave_max_q / float(j)))
s = s2
ep_reward += r
if terminal:
# summary_str = sess.run(summary_ops, feed_dict={
# summary_vars[0]: ep_reward,
# summary_vars[1]: ep_ave_max_q / float(j)
# })
# writer.add_summary(summary_str, i)
# writer.flush()
# print '| Reward: %.2i' % int(ep_reward), " | Episode", i, \
# '| Qmax: %.4f' % (ep_ave_max_q / float(j))
break
def reset(self): self.state = rnn_init_state(self.rnn)
vae.load_json(os.path.join('vae', 'vae.json'))
# Fourth, build the RNN
hps_atari_sample = hps_sample._replace(input_seq_width=z_size+na)
OUTWIDTH = hps_atari_sample.output_seq_width
rnn = MDNRNN(hps_atari_sample, gpu_mode=False)
rnn.load_json(os.path.join('rnn', 'rnn.json'))
print("All model loaded.")
# Fifth, run the evaluation. -> We have no predictions about the first frame.
start = time.time()
state = rnn_init_state(rnn) # initialize the state.
pz = None
for i in range(steps):
ob = obs[i:i+1] # (1, 64, 64, 1)
action = oh_actions[i:i+1] # (1, n)
z = vae.encode(ob) # (1, 32) VAE done!
rnn_z = np.expand_dims(z, axis=0) # (1, 1, 32)
action = np.expand_dims(action, axis=0) # (1, 1, n)
input_x = np.concatenate([rnn_z, action], axis=2) # (1, 1, 32+n)
feed = {rnn.input_x: input_x, rnn.initial_state: state} # predict the next state and next z.
