class PPOPolicy(TFPolicy):
def __init__(self, seed, brain, trainer_params, is_training, load):
"""
Policy for Proximal Policy Optimization Networks.
:param seed: Random seed.
:param brain: Assigned Brain object.
:param trainer_params: Defined training parameters.
:param is_training: Whether the model should be trained.
:param load: Whether a pre-trained model will be loaded or a new one created.
"""
super().__init__(seed, brain, trainer_params)
reward_signal_configs = trainer_params["reward_signals"]
self.reward_signals = {}
with self.graph.as_default():
self.model = PPOModel(
brain,
lr=float(trainer_params["learning_rate"]),
h_size=int(trainer_params["hidden_units"]),
epsilon=float(trainer_params["epsilon"]),
beta=float(trainer_params["beta"]),
max_step=float(trainer_params["max_steps"]),
normalize=trainer_params["normalize"],
use_recurrent=trainer_params["use_recurrent"],
num_layers=int(trainer_params["num_layers"]),
m_size=self.m_size,
seed=seed,
stream_names=list(reward_signal_configs.keys()),
vis_encode_type=EncoderType(
trainer_params.get("vis_encode_type", "simple")),
)
self.model.create_ppo_optimizer()
# Create reward signals
for reward_signal, config in reward_signal_configs.items():
self.reward_signals[reward_signal] = create_reward_signal(
self, reward_signal, config)
# Create pretrainer if needed
if "pretraining" in trainer_params:
BCModule.check_config(trainer_params["pretraining"])
self.bc_module = BCModule(
self,
policy_learning_rate=trainer_params["learning_rate"],
default_batch_size=trainer_params["batch_size"],
default_num_epoch=trainer_params["num_epoch"],
**trainer_params["pretraining"],
)
else:
self.bc_module = None
if load:
self._load_graph()
else:
self._initialize_graph()
self.inference_dict = {
"action": self.model.output,
"log_probs": self.model.all_log_probs,
"value": self.model.value_heads,
"entropy": self.model.entropy,
"learning_rate": self.model.learning_rate,
}
if self.use_continuous_act:
self.inference_dict["pre_action"] = self.model.output_pre
if self.use_recurrent:
self.inference_dict["memory_out"] = self.model.memory_out
if (is_training and self.use_vec_obs and trainer_params["normalize"]
and not load):
self.inference_dict[
"update_mean"] = self.model.update_normalization
self.total_policy_loss = self.model.policy_loss
self.update_dict = {
"value_loss": self.model.value_loss,
"policy_loss": self.total_policy_loss,
"update_batch": self.model.update_batch,
}
@timed
def evaluate(self, brain_info):
"""
Evaluates policy for the agent experiences provided.
:param brain_info: BrainInfo object containing inputs.
:return: Outputs from network as defined by self.inference_dict.
"""
feed_dict = {
self.model.batch_size: len(brain_info.vector_observations),
self.model.sequence_length: 1,
}
epsilon = None
if self.use_recurrent:
if not self.use_continuous_act:
feed_dict[
self.model.
prev_action] = brain_info.previous_vector_actions.reshape(
[-1, len(self.model.act_size)])
if brain_info.memories.shape[1] == 0:
brain_info.memories = self.make_empty_memory(
len(brain_info.agents))
feed_dict[self.model.memory_in] = brain_info.memories
if self.use_continuous_act:
epsilon = np.random.normal(
size=(len(brain_info.vector_observations),
self.model.act_size[0]))
feed_dict[self.model.epsilon] = epsilon
feed_dict = self.fill_eval_dict(feed_dict, brain_info)
run_out = self._execute_model(feed_dict, self.inference_dict)
if self.use_continuous_act:
run_out["random_normal_epsilon"] = epsilon
return run_out
@timed
def update(self, mini_batch, num_sequences):
"""
Updates model using buffer.
:param num_sequences: Number of trajectories in batch.
:param mini_batch: Experience batch.
:return: Output from update process.
"""
feed_dict = {
self.model.batch_size:
num_sequences,
self.model.sequence_length:
self.sequence_length,
self.model.mask_input:
mini_batch["masks"].flatten(),
self.model.advantage:
mini_batch["advantages"].reshape([-1, 1]),
self.model.all_old_log_probs:
mini_batch["action_probs"].reshape([-1,
sum(self.model.act_size)]),
}
for name in self.reward_signals:
feed_dict[self.model.returns_holders[name]] = mini_batch[
"{}_returns".format(name)].flatten()
feed_dict[self.model.old_values[name]] = mini_batch[
"{}_value_estimates".format(name)].flatten()
if self.use_continuous_act:
feed_dict[
self.model.output_pre] = mini_batch["actions_pre"].reshape(
[-1, self.model.act_size[0]])
feed_dict[self.model.
epsilon] = mini_batch["random_normal_epsilon"].reshape(
[-1, self.model.act_size[0]])
else:
feed_dict[
self.model.action_holder] = mini_batch["actions"].reshape(
[-1, len(self.model.act_size)])
if self.use_recurrent:
feed_dict[self.model.
prev_action] = mini_batch["prev_action"].reshape(
[-1, len(self.model.act_size)])
feed_dict[
self.model.action_masks] = mini_batch["action_mask"].reshape(
[-1, sum(self.brain.vector_action_space_size)])
if self.use_vec_obs:
feed_dict[self.model.vector_in] = mini_batch["vector_obs"].reshape(
[-1, self.vec_obs_size])
if self.model.vis_obs_size > 0:
for i, _ in enumerate(self.model.visual_in):
_obs = mini_batch["visual_obs%d" % i]
if self.sequence_length > 1 and self.use_recurrent:
(_batch, _seq, _w, _h, _c) = _obs.shape
feed_dict[self.model.visual_in[i]] = _obs.reshape(
[-1, _w, _h, _c])
else:
feed_dict[self.model.visual_in[i]] = _obs
if self.use_recurrent:
mem_in = mini_batch["memory"][:, 0, :]
feed_dict[self.model.memory_in] = mem_in
run_out = self._execute_model(feed_dict, self.update_dict)
return run_out
def get_value_estimates(self, brain_info: BrainInfo, idx: int,
done: bool) -> Dict[str, float]:
"""
Generates value estimates for bootstrapping.
:param brain_info: BrainInfo to be used for bootstrapping.
:param idx: Index in BrainInfo of agent.
:param done: Whether or not this is the last element of the episode, in which case the value estimate will be 0.
:return: The value estimate dictionary with key being the name of the reward signal and the value the
corresponding value estimate.
"""
feed_dict: Dict[tf.Tensor, Any] = {
self.model.batch_size: 1,
self.model.sequence_length: 1,
}
for i in range(len(brain_info.visual_observations)):
feed_dict[self.model.visual_in[i]] = [
brain_info.visual_observations[i][idx]
]
if self.use_vec_obs:
feed_dict[self.model.vector_in] = [
brain_info.vector_observations[idx]
]
if self.use_recurrent:
if brain_info.memories.shape[1] == 0:
brain_info.memories = self.make_empty_memory(
len(brain_info.agents))
feed_dict[self.model.memory_in] = [brain_info.memories[idx]]
if not self.use_continuous_act and self.use_recurrent:
feed_dict[
self.model.
prev_action] = brain_info.previous_vector_actions[idx].reshape(
[-1, len(self.model.act_size)])
value_estimates = self.sess.run(self.model.value_heads, feed_dict)
value_estimates = {k: float(v) for k, v in value_estimates.items()}
# If we're done, reassign all of the value estimates that need terminal states.
if done:
for k in value_estimates:
if self.reward_signals[k].use_terminal_states:
value_estimates[k] = 0.0
return value_estimates
def get_action(self, brain_info: BrainInfo) -> ActionInfo:
"""
Decides actions given observations information, and takes them in environment.
:param brain_info: A dictionary of brain names and BrainInfo from environment.
:return: an ActionInfo containing action, memories, values and an object
to be passed to add experiences
"""
if len(brain_info.agents) == 0:
return ActionInfo([], [], [], None, None)
run_out = self.evaluate(brain_info)
mean_values = np.mean(np.array(list(run_out.get("value").values())),
axis=0).flatten()
return ActionInfo(
action=run_out.get("action"),
memory=run_out.get("memory_out"),
text=None,
value=mean_values,
outputs=run_out,
)
class PPOPolicy(TFPolicy):
def __init__(
self,
seed: int,
brain: BrainParameters,
trainer_params: Dict[str, Any],
is_training: bool,
load: bool,
):
"""
Policy for Proximal Policy Optimization Networks.
:param seed: Random seed.
:param brain: Assigned Brain object.
:param trainer_params: Defined training parameters.
:param is_training: Whether the model should be trained.
:param load: Whether a pre-trained model will be loaded or a new one created.
"""
super().__init__(seed, brain, trainer_params)
reward_signal_configs = trainer_params["reward_signals"]
self.inference_dict: Dict[str, tf.Tensor] = {}
self.update_dict: Dict[str, tf.Tensor] = {}
self.stats_name_to_update_name = {
"Losses/Value Loss": "value_loss",
"Losses/Policy Loss": "policy_loss",
}
self.create_model(brain, trainer_params, reward_signal_configs,
is_training, load, seed)
self.create_reward_signals(reward_signal_configs)
with self.graph.as_default():
self.bc_module: Optional[BCModule] = None
# Create pretrainer if needed
if "pretraining" in trainer_params:
BCModule.check_config(trainer_params["pretraining"])
self.bc_module = BCModule(
self,
policy_learning_rate=trainer_params["learning_rate"],
default_batch_size=trainer_params["batch_size"],
default_num_epoch=trainer_params["num_epoch"],
**trainer_params["pretraining"],
)
if load:
self._load_graph()
else:
self._initialize_graph()
def create_model(self, brain, trainer_params, reward_signal_configs,
is_training, load, seed):
"""
Create PPO model
:param brain: Assigned Brain object.
:param trainer_params: Defined training parameters.
:param reward_signal_configs: Reward signal config
:param seed: Random seed.
"""
with self.graph.as_default():
self.model = PPOModel(
brain=brain,
lr=float(trainer_params["learning_rate"]),
lr_schedule=LearningRateSchedule(
trainer_params.get("learning_rate_schedule", "linear")),
h_size=int(trainer_params["hidden_units"]),
epsilon=float(trainer_params["epsilon"]),
beta=float(trainer_params["beta"]),
max_step=float(trainer_params["max_steps"]),
normalize=trainer_params["normalize"],
use_recurrent=trainer_params["use_recurrent"],
num_layers=int(trainer_params["num_layers"]),
m_size=self.m_size,
seed=seed,
stream_names=list(reward_signal_configs.keys()),
vis_encode_type=EncoderType(
trainer_params.get("vis_encode_type", "simple")),
)
self.model.create_ppo_optimizer()
self.inference_dict.update({
"action": self.model.output,
"log_probs": self.model.all_log_probs,
"value_heads": self.model.value_heads,
"value": self.model.value,
"entropy": self.model.entropy,
"learning_rate": self.model.learning_rate,
})
if self.use_continuous_act:
self.inference_dict["pre_action"] = self.model.output_pre
if self.use_recurrent:
self.inference_dict["memory_out"] = self.model.memory_out
self.total_policy_loss = self.model.abs_policy_loss
self.update_dict.update({
"value_loss": self.model.value_loss,
"policy_loss": self.total_policy_loss,
"update_batch": self.model.update_batch,
})
def create_reward_signals(self, reward_signal_configs):
"""
Create reward signals
:param reward_signal_configs: Reward signal config.
"""
self.reward_signals = {}
with self.graph.as_default():
# Create reward signals
for reward_signal, config in reward_signal_configs.items():
self.reward_signals[reward_signal] = create_reward_signal(
self, self.model, reward_signal, config)
self.update_dict.update(
self.reward_signals[reward_signal].update_dict)
@timed
def evaluate(self, brain_info):
"""
Evaluates policy for the agent experiences provided.
:param brain_info: BrainInfo object containing inputs.
:return: Outputs from network as defined by self.inference_dict.
"""
feed_dict = {
self.model.batch_size: len(brain_info.vector_observations),
self.model.sequence_length: 1,
}
epsilon = None
if self.use_recurrent:
if not self.use_continuous_act:
feed_dict[
self.model.
prev_action] = brain_info.previous_vector_actions.reshape(
[-1, len(self.model.act_size)])
if brain_info.memories.shape[1] == 0:
brain_info.memories = self.make_empty_memory(
len(brain_info.agents))
feed_dict[self.model.memory_in] = brain_info.memories
if self.use_continuous_act:
epsilon = np.random.normal(
size=(len(brain_info.vector_observations),
self.model.act_size[0]))
feed_dict[self.model.epsilon] = epsilon
feed_dict = self.fill_eval_dict(feed_dict, brain_info)
run_out = self._execute_model(feed_dict, self.inference_dict)
if self.use_continuous_act:
run_out["random_normal_epsilon"] = epsilon
return run_out
@timed
def update(self, mini_batch, num_sequences):
"""
Performs update on model.
:param mini_batch: Batch of experiences.
:param num_sequences: Number of sequences to process.
:return: Results of update.
"""
feed_dict = self.construct_feed_dict(self.model, mini_batch,
num_sequences)
stats_needed = self.stats_name_to_update_name
update_stats = {}
# Collect feed dicts for all reward signals.
for _, reward_signal in self.reward_signals.items():
feed_dict.update(
reward_signal.prepare_update(self.model, mini_batch,
num_sequences))
stats_needed.update(reward_signal.stats_name_to_update_name)
update_vals = self._execute_model(feed_dict, self.update_dict)
for stat_name, update_name in stats_needed.items():
update_stats[stat_name] = update_vals[update_name]
return update_stats
def construct_feed_dict(self, model, mini_batch, num_sequences):
feed_dict = {
model.batch_size: num_sequences,
model.sequence_length: self.sequence_length,
model.mask_input: mini_batch["masks"],
model.advantage: mini_batch["advantages"],
model.all_old_log_probs: mini_batch["action_probs"],
}
for name in self.reward_signals:
feed_dict[model.returns_holders[name]] = mini_batch[
"{}_returns".format(name)]
feed_dict[model.old_values[name]] = mini_batch[
"{}_value_estimates".format(name)]
if self.use_continuous_act:
feed_dict[model.output_pre] = mini_batch["actions_pre"]
feed_dict[model.epsilon] = mini_batch["random_normal_epsilon"]
else:
feed_dict[model.action_holder] = mini_batch["actions"]
if self.use_recurrent:
feed_dict[model.prev_action] = mini_batch["prev_action"]
feed_dict[model.action_masks] = mini_batch["action_mask"]
if self.use_vec_obs:
feed_dict[model.vector_in] = mini_batch["vector_obs"]
if self.model.vis_obs_size > 0:
for i, _ in enumerate(self.model.visual_in):
feed_dict[model.visual_in[i]] = mini_batch["visual_obs%d" % i]
if self.use_recurrent:
mem_in = [
mini_batch["memory"][i] for i in range(
0, len(mini_batch["memory"]), self.sequence_length)
]
feed_dict[model.memory_in] = mem_in
return feed_dict
def get_value_estimates(self, brain_info: BrainInfo, idx: int,
done: bool) -> Dict[str, float]:
"""
Generates value estimates for bootstrapping.
:param brain_info: BrainInfo to be used for bootstrapping.
:param idx: Index in BrainInfo of agent.
:param done: Whether or not this is the last element of the episode, in which case the value estimate will be 0.
:return: The value estimate dictionary with key being the name of the reward signal and the value the
corresponding value estimate.
"""
feed_dict: Dict[tf.Tensor, Any] = {
self.model.batch_size: 1,
self.model.sequence_length: 1,
}
for i in range(len(brain_info.visual_observations)):
feed_dict[self.model.visual_in[i]] = [
brain_info.visual_observations[i][idx]
]
if self.use_vec_obs:
feed_dict[self.model.vector_in] = [
brain_info.vector_observations[idx]
]
if self.use_recurrent:
if brain_info.memories.shape[1] == 0:
brain_info.memories = self.make_empty_memory(
len(brain_info.agents))
feed_dict[self.model.memory_in] = [brain_info.memories[idx]]
if not self.use_continuous_act and self.use_recurrent:
feed_dict[self.model.prev_action] = [
brain_info.previous_vector_actions[idx]
]
value_estimates = self.sess.run(self.model.value_heads, feed_dict)
value_estimates = {k: float(v) for k, v in value_estimates.items()}
# If we're done, reassign all of the value estimates that need terminal states.
if done:
for k in value_estimates:
if self.reward_signals[k].use_terminal_states:
value_estimates[k] = 0.0
return value_estimates
class PPOPolicy(TFPolicy):
def __init__(
self,
seed: int,
brain: BrainParameters,
trainer_params: Dict[str, Any],
is_training: bool,
load: bool,
):
"""
Policy for Proximal Policy Optimization Networks.
:param seed: Random seed.
:param brain: Assigned Brain object.
:param trainer_params: Defined training parameters.
:param is_training: Whether the model should be trained.
:param load: Whether a pre-trained model will be loaded or a new one created.
"""
super().__init__(seed, brain, trainer_params)
reward_signal_configs = trainer_params["reward_signals"]
self.inference_dict: Dict[str, tf.Tensor] = {}
self.update_dict: Dict[str, tf.Tensor] = {}
self.stats_name_to_update_name = {
"Losses/Value Loss": "value_loss",
"Losses/Policy Loss": "policy_loss",
}
self.create_model(
brain, trainer_params, reward_signal_configs, is_training, load, seed
)
self.create_reward_signals(reward_signal_configs)
with self.graph.as_default():
self.bc_module: Optional[BCModule] = None
# Create pretrainer if needed
if "behavioral_cloning" in trainer_params:
BCModule.check_config(trainer_params["behavioral_cloning"])
self.bc_module = BCModule(
self,
policy_learning_rate=trainer_params["learning_rate"],
default_batch_size=trainer_params["batch_size"],
default_num_epoch=3,
**trainer_params["behavioral_cloning"],
)
if load:
self._load_graph()
else:
self._initialize_graph()
def create_model(
self, brain, trainer_params, reward_signal_configs, is_training, load, seed
):
"""
Create PPO model
:param brain: Assigned Brain object.
:param trainer_params: Defined training parameters.
:param reward_signal_configs: Reward signal config
:param seed: Random seed.
"""
with self.graph.as_default():
self.model = PPOModel(
brain=brain,
lr=float(trainer_params["learning_rate"]),
lr_schedule=LearningRateSchedule(
trainer_params.get("learning_rate_schedule", "linear")
),
h_size=int(trainer_params["hidden_units"]),
epsilon=float(trainer_params["epsilon"]),
beta=float(trainer_params["beta"]),
max_step=float(trainer_params["max_steps"]),
normalize=trainer_params["normalize"],
use_recurrent=trainer_params["use_recurrent"],
num_layers=int(trainer_params["num_layers"]),
m_size=self.m_size,
seed=seed,
stream_names=list(reward_signal_configs.keys()),
vis_encode_type=EncoderType(
trainer_params.get("vis_encode_type", "simple")
),
)
self.model.create_ppo_optimizer()
self.inference_dict.update(
{
"action": self.model.output,
"log_probs": self.model.all_log_probs,
"entropy": self.model.entropy,
"learning_rate": self.model.learning_rate,
}
)
if self.use_continuous_act:
self.inference_dict["pre_action"] = self.model.output_pre
if self.use_recurrent:
self.inference_dict["memory_out"] = self.model.memory_out
self.total_policy_loss = self.model.abs_policy_loss
self.update_dict.update(
{
"value_loss": self.model.value_loss,
"policy_loss": self.total_policy_loss,
"update_batch": self.model.update_batch,
}
)
def create_reward_signals(self, reward_signal_configs):
"""
Create reward signals
:param reward_signal_configs: Reward signal config.
"""
self.reward_signals = {}
with self.graph.as_default():
# Create reward signals
for reward_signal, config in reward_signal_configs.items():
self.reward_signals[reward_signal] = create_reward_signal(
self, self.model, reward_signal, config
)
self.update_dict.update(self.reward_signals[reward_signal].update_dict)
@timed
def evaluate(
self, batched_step_result: BatchedStepResult, global_agent_ids: List[str]
) -> Dict[str, Any]:
"""
Evaluates policy for the agent experiences provided.
:param batched_step_result: BatchedStepResult object containing inputs.
:param global_agent_ids: The global (with worker ID) agent ids of the data in the batched_step_result.
:return: Outputs from network as defined by self.inference_dict.
"""
feed_dict = {
self.model.batch_size: batched_step_result.n_agents(),
self.model.sequence_length: 1,
}
epsilon = None
if self.use_recurrent:
if not self.use_continuous_act:
feed_dict[self.model.prev_action] = self.retrieve_previous_action(
global_agent_ids
)
feed_dict[self.model.memory_in] = self.retrieve_memories(global_agent_ids)
if self.use_continuous_act:
epsilon = np.random.normal(
size=(batched_step_result.n_agents(), self.model.act_size[0])
)
feed_dict[self.model.epsilon] = epsilon
feed_dict = self.fill_eval_dict(feed_dict, batched_step_result)
run_out = self._execute_model(feed_dict, self.inference_dict)
return run_out
@timed
def update(self, mini_batch, num_sequences):
"""
Performs update on model.
:param mini_batch: Batch of experiences.
:param num_sequences: Number of sequences to process.
:return: Results of update.
"""
feed_dict = self.construct_feed_dict(self.model, mini_batch, num_sequences)
stats_needed = self.stats_name_to_update_name
update_stats = {}
# Collect feed dicts for all reward signals.
for _, reward_signal in self.reward_signals.items():
feed_dict.update(
reward_signal.prepare_update(self.model, mini_batch, num_sequences)
)
stats_needed.update(reward_signal.stats_name_to_update_name)
update_vals = self._execute_model(feed_dict, self.update_dict)
for stat_name, update_name in stats_needed.items():
update_stats[stat_name] = update_vals[update_name]
return update_stats
def construct_feed_dict(self, model, mini_batch, num_sequences):
feed_dict = {
model.batch_size: num_sequences,
model.sequence_length: self.sequence_length,
model.mask_input: mini_batch["masks"],
model.advantage: mini_batch["advantages"],
model.all_old_log_probs: mini_batch["action_probs"],
}
for name in self.reward_signals:
feed_dict[model.returns_holders[name]] = mini_batch[
"{}_returns".format(name)
]
feed_dict[model.old_values[name]] = mini_batch[
"{}_value_estimates".format(name)
]
if self.use_continuous_act:
feed_dict[model.output_pre] = mini_batch["actions_pre"]
else:
feed_dict[model.action_holder] = mini_batch["actions"]
if self.use_recurrent:
feed_dict[model.prev_action] = mini_batch["prev_action"]
feed_dict[model.action_masks] = mini_batch["action_mask"]
if self.use_vec_obs:
feed_dict[model.vector_in] = mini_batch["vector_obs"]
if self.model.vis_obs_size > 0:
for i, _ in enumerate(self.model.visual_in):
feed_dict[model.visual_in[i]] = mini_batch["visual_obs%d" % i]
if self.use_recurrent:
mem_in = [
mini_batch["memory"][i]
for i in range(0, len(mini_batch["memory"]), self.sequence_length)
]
feed_dict[model.memory_in] = mem_in
return feed_dict
