def test_transition_data(self):
env = make('Acrobot-v1')
env_spec = EnvSpec(obs_space=env.observation_space,
action_space=env.action_space)
a = UniformRandomReplayBuffer(limit=10000,
action_shape=env_spec.action_shape,
observation_shape=env_spec.obs_shape)
st = env.reset()
for i in range(100):
ac = env_spec.action_space.sample()
st_new, re, done, _ = env.step(action=ac)
a.append(obs0=st,
obs1=st_new,
action=ac,
reward=re,
terminal1=done)
st = st_new
batch = a.sample(batch_size=10)
self.assertTrue(batch.state_set.shape[0] == 10)
self.assertTrue(batch.action_set.shape[0] == 10)
self.assertTrue(batch.reward_set.shape[0] == 10)
self.assertTrue(batch.done_set.shape[0] == 10)
self.assertTrue(batch.new_state_set.shape[0] == 10)
class DQN(ModelFreeAlgo, OffPolicyAlgo, MultiPlaceholderInput):
required_key_dict = DictConfig.load_json(file_path=GlobalConfig().DEFAULT_DQN_REQUIRED_KEY_LIST)
@init_func_arg_record_decorator()
@typechecked
def __init__(self,
env_spec,
config_or_config_dict: (DictConfig, dict),
value_func: MLPQValueFunction,
schedule_param_list=None,
name: str = 'dqn',
replay_buffer=None):
ModelFreeAlgo.__init__(self, env_spec=env_spec, name=name)
self.config = construct_dict_config(config_or_config_dict, self)
if replay_buffer:
assert issubclass(replay_buffer, BaseReplayBuffer)
self.replay_buffer = replay_buffer
else:
self.replay_buffer = UniformRandomReplayBuffer(limit=self.config('REPLAY_BUFFER_SIZE'),
action_shape=self.env_spec.action_shape,
observation_shape=self.env_spec.obs_shape)
self.q_value_func = value_func
self.state_input = self.q_value_func.state_input
self.action_input = self.q_value_func.action_input
self.update_target_q_every_train = self.config('UPDATE_TARGET_Q_FREQUENCY') if 'UPDATE_TARGET_Q_FREQUENCY' in \
self.config.config_dict else 1
self.parameters = ParametersWithTensorflowVariable(tf_var_list=[],
rest_parameters=dict(),
to_scheduler_param_tuple=schedule_param_list,
name='{}_param'.format(name),
source_config=self.config,
require_snapshot=False)
with tf.variable_scope(name):
self.reward_input = tf.placeholder(shape=[None, 1], dtype=tf.float32)
self.next_state_input = tf.placeholder(shape=[None, self.env_spec.flat_obs_dim], dtype=tf.float32)
self.done_input = tf.placeholder(shape=[None, 1], dtype=tf.bool)
self.target_q_input = tf.placeholder(shape=[None, 1], dtype=tf.float32)
done = tf.cast(self.done_input, dtype=tf.float32)
self.target_q_value_func = self.q_value_func.make_copy(name_scope='{}_target_q_value_net'.format(name),
name='{}_target_q_value_net'.format(name),
reuse=False)
self.predict_q_value = (1. - done) * self.config('GAMMA') * self.target_q_input + self.reward_input
self.td_error = self.predict_q_value - self.q_value_func.q_tensor
with tf.variable_scope('train'):
self.q_value_func_loss, self.optimizer, self.update_q_value_func_op = self._set_up_loss()
self.update_target_q_value_func_op = self._set_up_target_update()
# redundant sort operation on var_list
var_list = get_tf_collection_var_list(key=tf.GraphKeys.GLOBAL_VARIABLES,
scope='{}/train'.format(name)) + self.optimizer.variables()
self.parameters.set_tf_var_list(tf_var_list=sorted(list(set(var_list)), key=lambda x: x.name))
MultiPlaceholderInput.__init__(self,
sub_placeholder_input_list=[dict(obj=self.q_value_func, attr_name='q_value_func'),
dict(obj=self.target_q_value_func, attr_name='target_q_value_func')],
parameters=self.parameters)
@register_counter_info_to_status_decorator(increment=1, info_key='init', under_status='INITED')
def init(self, sess=None, source_obj=None):
super().init()
self.q_value_func.init()
self.target_q_value_func.init(source_obj=self.q_value_func)
self.parameters.init()
if source_obj:
self.copy_from(source_obj)
@record_return_decorator(which_recorder='self')
@register_counter_info_to_status_decorator(increment=1, info_key='train_counter', under_status='TRAIN')
def train(self, batch_data=None, train_iter=None, sess=None, update_target=True) -> dict:
super(DQN, self).train()
self.recorder.record()
if batch_data and not isinstance(batch_data, TransitionData):
raise TypeError()
tf_sess = sess if sess else tf.get_default_session()
train_iter = self.parameters("TRAIN_ITERATION") if not train_iter else train_iter
average_loss = 0.0
for i in range(train_iter):
if batch_data is None:
train_data = self.replay_buffer.sample(batch_size=self.parameters('BATCH_SIZE'))
else:
train_data = batch_data
_, target_q_val_on_new_s = self.predict_target_with_q_val(obs=train_data.new_state_set, batch_flag=True)
target_q_val_on_new_s = np.expand_dims(target_q_val_on_new_s, axis=1)
assert target_q_val_on_new_s.shape[0] == train_data.state_set.shape[0]
feed_dict = {
self.reward_input: np.reshape(train_data.reward_set, [-1, 1]),
self.action_input: flatten_n(self.env_spec.action_space, train_data.action_set),
self.state_input: train_data.state_set,
self.done_input: np.reshape(train_data.done_set, [-1, 1]),
self.target_q_input: target_q_val_on_new_s,
**self.parameters.return_tf_parameter_feed_dict()
}
res, _ = tf_sess.run([self.q_value_func_loss, self.update_q_value_func_op],
feed_dict=feed_dict)
average_loss += res
average_loss /= train_iter
if update_target is True and self.get_status()['train_counter'] % self.update_target_q_every_train == 0:
tf_sess.run(self.update_target_q_value_func_op,
feed_dict=self.parameters.return_tf_parameter_feed_dict())
return dict(average_loss=average_loss)
@register_counter_info_to_status_decorator(increment=1, info_key='test_counter', under_status='TEST')
def test(self, *arg, **kwargs):
return super().test(*arg, **kwargs)
@register_counter_info_to_status_decorator(increment=1, info_key='predict_counter')
def predict(self, obs: np.ndarray, sess=None, batch_flag: bool = False):
if batch_flag:
action, q_val = self._predict_batch_action(obs=obs,
q_value_tensor=self.q_value_func.q_tensor,
action_ph=self.action_input,
state_ph=self.state_input,
sess=sess)
else:
action, q_val = self._predict_action(obs=obs,
q_value_tensor=self.q_value_func.q_tensor,
action_ph=self.action_input,
state_ph=self.state_input,
sess=sess)
if not batch_flag:
return int(action)
else:
return action.astype(np.int).tolist()
def predict_target_with_q_val(self, obs: np.ndarray, sess=None, batch_flag: bool = False):
if batch_flag:
action, q_val = self._predict_batch_action(obs=obs,
q_value_tensor=self.target_q_value_func.q_tensor,
action_ph=self.target_q_value_func.action_input,
state_ph=self.target_q_value_func.state_input,
sess=sess)
else:
action, q_val = self._predict_action(obs=obs,
q_value_tensor=self.target_q_value_func.q_tensor,
action_ph=self.target_q_value_func.action_input,
state_ph=self.target_q_value_func.state_input,
sess=sess)
return action, q_val
# Store Transition
@register_counter_info_to_status_decorator(increment=1, info_key='append_to_memory')
def append_to_memory(self, samples: TransitionData):
self.replay_buffer.append_batch(obs0=samples.state_set,
obs1=samples.new_state_set,
action=samples.action_set,
reward=samples.reward_set,
terminal1=samples.done_set)
self._status.update_info(info_key='replay_buffer_data_total_count', increment=len(samples))
@record_return_decorator(which_recorder='self')
def save(self, global_step, save_path=None, name=None, **kwargs):
save_path = save_path if save_path else GlobalConfig().DEFAULT_MODEL_CHECKPOINT_PATH
name = name if name else self.name
MultiPlaceholderInput.save(self, save_path=save_path, global_step=global_step, name=name, **kwargs)
return dict(check_point_save_path=save_path, check_point_save_global_step=global_step,
check_point_save_name=name)
@record_return_decorator(which_recorder='self')
def load(self, path_to_model, model_name, global_step=None, **kwargs):
MultiPlaceholderInput.load(self, path_to_model, model_name, global_step, **kwargs)
return dict(check_point_load_path=path_to_model, check_point_load_global_step=global_step,
check_point_load_name=model_name)
def _predict_action(self, obs: np.ndarray, q_value_tensor: tf.Tensor, action_ph: tf.Tensor, state_ph: tf.Tensor, sess=None):
if self.env_spec.obs_space.contains(obs) is False:
raise StateOrActionOutOfBoundError("obs {} out of bound {}".format(obs, self.env_spec.obs_space.bound()))
obs = repeat_ndarray(obs, repeats=self.env_spec.flat_action_dim)
tf_sess = sess if sess else tf.get_default_session()
feed_dict = {action_ph: generate_n_actions_hot_code(n=self.env_spec.flat_action_dim),
state_ph: obs, **self.parameters.return_tf_parameter_feed_dict()}
res = tf_sess.run([q_value_tensor],
feed_dict=feed_dict)[0]
return np.argmax(res, axis=0), np.max(res, axis=0)
def _predict_batch_action(self, obs: np.ndarray, q_value_tensor: tf.Tensor, action_ph: tf.Tensor,
state_ph: tf.Tensor, sess=None):
actions = []
q_values = []
for obs_i in obs:
action, q_val = self._predict_action(obs=obs_i,
q_value_tensor=q_value_tensor,
action_ph=action_ph,
state_ph=state_ph,
sess=sess)
actions.append(np.argmax(action, axis=0))
q_values.append(np.max(q_val, axis=0))
return np.array(actions), np.array(q_values)
def _set_up_loss(self):
reg_loss = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES, scope=self.q_value_func.name_scope)
loss = tf.reduce_sum((self.predict_q_value - self.q_value_func.q_tensor) ** 2)
if len(reg_loss) > 0:
loss += tf.reduce_sum(reg_loss)
optimizer = tf.train.AdamOptimizer(learning_rate=self.parameters('LEARNING_RATE'))
optimize_op = optimizer.minimize(loss=loss, var_list=self.q_value_func.parameters('tf_var_list'))
return loss, optimizer, optimize_op
# update target net
def _set_up_target_update(self):
op = []
for var, target_var in zip(self.q_value_func.parameters('tf_var_list'),
self.target_q_value_func.parameters('tf_var_list')):
ref_val = self.parameters('DECAY') * target_var + (1.0 - self.parameters('DECAY')) * var
op.append(tf.assign(target_var, ref_val))
return op
def _evaluate_td_error(self, sess=None):
# tf_sess = sess if sess else tf.get_default_session()
# feed_dict = {
# self.reward_input: train_data.reward_set,
# self.action_input: flatten_n(self.env_spec.action_space, train_data.action_set),
# self.state_input: train_data.state_set,
# self.done_input: train_data.done_set,
# self.target_q_input: target_q_val_on_new_s,
# **self.parameters.return_tf_parameter_feed_dict()
# }
# td_loss = tf_sess.run([self.td_error], feed_dict=feed_dict)
pass
class DDPG(ModelFreeAlgo, OffPolicyAlgo, MultiPlaceholderInput):
required_key_dict = DictConfig.load_json(file_path=GlobalConfig().DEFAULT_DDPG_REQUIRED_KEY_LIST)
@typechecked
def __init__(self,
env_spec: EnvSpec,
config_or_config_dict: (DictConfig, dict),
value_func: MLPQValueFunction,
policy: DeterministicMLPPolicy,
schedule_param_list=None,
name='ddpg',
replay_buffer=None):
"""
:param env_spec: environment specifications, like action apace or observation space
:param config_or_config_dict: configuraion dictionary, like learning rate or decay, if any
:param value_func: value function
:param policy: agent policy
:param schedule_param_list: schedule parameter list, if any initla final function to schedule learning process
:param name: name of algorithm class instance
:param replay_buffer: replay buffer, if any
"""
ModelFreeAlgo.__init__(self, env_spec=env_spec, name=name)
config = construct_dict_config(config_or_config_dict, self)
self.config = config
self.actor = policy
self.target_actor = self.actor.make_copy(name_scope='{}_target_actor'.format(self.name),
name='{}_target_actor'.format(self.name),
reuse=False)
self.critic = value_func
self.target_critic = self.critic.make_copy(name_scope='{}_target_critic'.format(self.name),
name='{}_target_critic'.format(self.name),
reuse=False)
self.state_input = self.actor.state_input
if replay_buffer:
assert issubclass(replay_buffer, BaseReplayBuffer)
self.replay_buffer = replay_buffer
else:
self.replay_buffer = UniformRandomReplayBuffer(limit=self.config('REPLAY_BUFFER_SIZE'),
action_shape=self.env_spec.action_shape,
observation_shape=self.env_spec.obs_shape)
"""
self.parameters contains all the parameters (variables) of the algorithm
"""
self.parameters = ParametersWithTensorflowVariable(tf_var_list=[],
rest_parameters=dict(),
to_scheduler_param_tuple=schedule_param_list,
name='ddpg_param',
source_config=config,
require_snapshot=False)
self._critic_with_actor_output = self.critic.make_copy(reuse=True,
name='actor_input_{}'.format(self.critic.name),
state_input=self.state_input,
action_input=self.actor.action_tensor)
self._target_critic_with_target_actor_output = self.target_critic.make_copy(reuse=True,
name='target_critic_with_target_actor_output_{}'.format(
self.critic.name),
action_input=self.target_actor.action_tensor)
with tf.variable_scope(name):
self.reward_input = tf.placeholder(shape=[None, 1], dtype=tf.float32)
self.next_state_input = tf.placeholder(shape=[None, self.env_spec.flat_obs_dim], dtype=tf.float32)
self.done_input = tf.placeholder(shape=[None, 1], dtype=tf.bool)
self.target_q_input = tf.placeholder(shape=[None, 1], dtype=tf.float32)
done = tf.cast(self.done_input, dtype=tf.float32)
self.predict_q_value = (1. - done) * self.config('GAMMA') * self.target_q_input + self.reward_input
with tf.variable_scope('train'):
self.critic_loss, self.critic_update_op, self.target_critic_update_op, self.critic_optimizer, \
self.critic_grads = self._setup_critic_loss()
self.actor_loss, self.actor_update_op, self.target_actor_update_op, self.action_optimizer, \
self.actor_grads = self._set_up_actor_loss()
var_list = get_tf_collection_var_list(
'{}/train'.format(name)) + self.critic_optimizer.variables() + self.action_optimizer.variables()
self.parameters.set_tf_var_list(tf_var_list=sorted(list(set(var_list)), key=lambda x: x.name))
MultiPlaceholderInput.__init__(self,
sub_placeholder_input_list=[dict(obj=self.target_actor,
attr_name='target_actor',
),
dict(obj=self.actor,
attr_name='actor'),
dict(obj=self.critic,
attr_name='critic'),
dict(obj=self.target_critic,
attr_name='target_critic')
],
parameters=self.parameters)
@register_counter_info_to_status_decorator(increment=1, info_key='init', under_status='INITED')
def init(self, sess=None, source_obj=None):
self.actor.init()
self.critic.init()
self.target_actor.init()
self.target_critic.init(source_obj=self.critic)
self.parameters.init()
if source_obj:
self.copy_from(source_obj)
super().init()
@record_return_decorator(which_recorder='self')
@register_counter_info_to_status_decorator(increment=1, info_key='train', under_status='TRAIN')
def train(self, batch_data=None, train_iter=None, sess=None, update_target=True) -> dict:
super(DDPG, self).train()
if isinstance(batch_data, TrajectoryData):
batch_data = batch_data.return_as_transition_data(shuffle_flag=True)
tf_sess = sess if sess else tf.get_default_session()
train_iter = self.parameters("TRAIN_ITERATION") if not train_iter else train_iter
average_critic_loss = 0.0
average_actor_loss = 0.0
for i in range(train_iter):
train_batch = self.replay_buffer.sample(
batch_size=self.parameters('BATCH_SIZE')) if batch_data is None else batch_data
assert isinstance(train_batch, TransitionData)
critic_loss, _ = self._critic_train(train_batch, tf_sess)
actor_loss, _ = self._actor_train(train_batch, tf_sess)
average_actor_loss += actor_loss
average_critic_loss += critic_loss
if update_target:
tf_sess.run([self.target_actor_update_op, self.target_critic_update_op])
return dict(average_actor_loss=average_actor_loss / train_iter,
average_critic_loss=average_critic_loss / train_iter)
def _critic_train(self, batch_data, sess) -> ():
target_q = sess.run(
self._target_critic_with_target_actor_output.q_tensor,
feed_dict={
self._target_critic_with_target_actor_output.state_input: batch_data.new_state_set,
self.target_actor.state_input: batch_data.new_state_set
}
)
loss, _, grads = sess.run(
[self.critic_loss, self.critic_update_op, self.critic_grads
],
feed_dict={
self.target_q_input: target_q,
self.critic.state_input: batch_data.state_set,
self.critic.action_input: batch_data.action_set,
self.done_input: np.reshape(batch_data.done_set, [-1, 1]),
self.reward_input: np.reshape(batch_data.reward_set, [-1, 1]),
**self.parameters.return_tf_parameter_feed_dict()
}
)
return loss, grads
def _actor_train(self, batch_data, sess) -> ():
target_q, loss, _, grads = sess.run(
[self._critic_with_actor_output.q_tensor, self.actor_loss, self.actor_update_op, self.actor_grads],
feed_dict={
self.actor.state_input: batch_data.state_set,
self._critic_with_actor_output.state_input: batch_data.state_set,
**self.parameters.return_tf_parameter_feed_dict()
}
)
return loss, grads
@register_counter_info_to_status_decorator(increment=1, info_key='test', under_status='TEST')
def test(self, *arg, **kwargs) -> dict:
return super().test(*arg, **kwargs)
def predict(self, obs: np.ndarray, sess=None, batch_flag: bool = False):
tf_sess = sess if sess else tf.get_default_session()
feed_dict = {
self.state_input: make_batch(obs, original_shape=self.env_spec.obs_shape),
**self.parameters.return_tf_parameter_feed_dict()
}
return self.actor.forward(obs=obs, sess=tf_sess, feed_dict=feed_dict)
def append_to_memory(self, samples: TransitionData):
self.replay_buffer.append_batch(obs0=samples.state_set,
obs1=samples.new_state_set,
action=samples.action_set,
reward=samples.reward_set,
terminal1=samples.done_set)
@record_return_decorator(which_recorder='self')
def save(self, global_step, save_path=None, name=None, **kwargs):
save_path = save_path if save_path else GlobalConfig().DEFAULT_MODEL_CHECKPOINT_PATH
name = name if name else self.name
MultiPlaceholderInput.save(self, save_path=save_path, global_step=global_step, name=name, **kwargs)
return dict(check_point_save_path=save_path, check_point_save_global_step=global_step,
check_point_save_name=name)
@record_return_decorator(which_recorder='self')
def load(self, path_to_model, model_name, global_step=None, **kwargs):
MultiPlaceholderInput.load(self, path_to_model, model_name, global_step, **kwargs)
return dict(check_point_load_path=path_to_model, check_point_load_global_step=global_step,
check_point_load_name=model_name)
def _setup_critic_loss(self):
reg_loss = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES, scope=self.critic.name_scope)
loss = tf.reduce_sum((self.predict_q_value - self.critic.q_tensor) ** 2)
if len(reg_loss) > 0:
loss += tf.reduce_sum(reg_loss)
optimizer = tf.train.AdamOptimizer(learning_rate=self.parameters('CRITIC_LEARNING_RATE'))
grad_var_pair = optimizer.compute_gradients(loss=loss, var_list=self.critic.parameters('tf_var_list'))
grads = [g[0] for g in grad_var_pair]
if self.parameters('critic_clip_norm') is not None:
grad_var_pair, grads = clip_grad(optimizer=optimizer,
loss=loss,
var_list=self.critic.parameters('tf_var_list'),
clip_norm=self.parameters('critic_clip_norm'))
optimize_op = optimizer.apply_gradients(grad_var_pair)
op = []
for var, target_var in zip(self.critic.parameters('tf_var_list'),
self.target_critic.parameters('tf_var_list')):
ref_val = self.parameters('DECAY') * target_var + (1.0 - self.parameters('DECAY')) * var
op.append(tf.assign(target_var, ref_val))
return loss, optimize_op, op, optimizer, grads
def _set_up_actor_loss(self):
reg_loss = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES, scope=self.actor.name_scope)
loss = -tf.reduce_mean(self._critic_with_actor_output.q_tensor)
if len(reg_loss) > 0:
loss += tf.reduce_sum(reg_loss)
optimizer = tf.train.AdamOptimizer(learning_rate=self.parameters('CRITIC_LEARNING_RATE'))
grad_var_pair = optimizer.compute_gradients(loss=loss, var_list=self.actor.parameters('tf_var_list'))
grads = [g[0] for g in grad_var_pair]
if self.parameters('actor_clip_norm') is not None:
grad_var_pair, grads = clip_grad(optimizer=optimizer,
loss=loss,
var_list=self.actor.parameters('tf_var_list'),
clip_norm=self.parameters('critic_clip_norm'))
optimize_op = optimizer.apply_gradients(grad_var_pair)
op = []
for var, target_var in zip(self.actor.parameters('tf_var_list'),
self.target_actor.parameters('tf_var_list')):
ref_val = self.parameters('DECAY') * target_var + (1.0 - self.parameters('DECAY')) * var
op.append(tf.assign(target_var, ref_val))
return loss, optimize_op, op, optimizer, grads