您最多选择25个主题
主题必须以中文或者字母或数字开头，可以包含连字符 (-)，并且长度不得超过35个字符
83 行
3.5 KiB
83 行
3.5 KiB
import logging
|
|
from typing import Any, Dict, List
|
|
from collections import namedtuple
|
|
import numpy as np
|
|
import abc
|
|
|
|
from mlagents.tf_utils import tf
|
|
|
|
from mlagents.trainers.exception import UnityTrainerException
|
|
from mlagents.trainers.policy.tf_policy import TFPolicy
|
|
from mlagents.trainers.buffer import AgentBuffer
|
|
|
|
logger = logging.getLogger("mlagents.trainers")
|
|
|
|
RewardSignalResult = namedtuple(
|
|
"RewardSignalResult", ["scaled_reward", "unscaled_reward"]
|
|
)
|
|
|
|
|
|
class RewardSignal(abc.ABC):
|
|
def __init__(self, policy: TFPolicy, strength: float, gamma: float):
|
|
"""
|
|
Initializes a reward signal. At minimum, you must pass in the policy it is being applied to,
|
|
the reward strength, and the gamma (discount factor.)
|
|
:param policy: The Policy object (e.g. NNPolicy) that this Reward Signal will apply to.
|
|
:param strength: The strength of the reward. The reward's raw value will be multiplied by this value.
|
|
:param gamma: The time discounting factor used for this reward.
|
|
:return: A RewardSignal object.
|
|
"""
|
|
class_name = self.__class__.__name__
|
|
short_name = class_name.replace("RewardSignal", "")
|
|
self.stat_name = f"Policy/{short_name} Reward"
|
|
self.value_name = f"Policy/{short_name} Value Estimate"
|
|
# Terminate discounted reward computation at Done. Can disable to mitigate positive bias in rewards with
|
|
# no natural end, e.g. GAIL or Curiosity
|
|
self.use_terminal_states = True
|
|
self.update_dict: Dict[str, tf.Tensor] = {}
|
|
self.gamma = gamma
|
|
self.policy = policy
|
|
self.strength = strength
|
|
self.stats_name_to_update_name: Dict[str, str] = {}
|
|
|
|
def evaluate_batch(self, mini_batch: AgentBuffer) -> RewardSignalResult:
|
|
"""
|
|
Evaluates the reward for the data present in the Dict mini_batch. Use this when evaluating a reward
|
|
function drawn straight from a Buffer.
|
|
:param mini_batch: A Dict of numpy arrays (the format used by our Buffer)
|
|
when drawing from the update buffer.
|
|
:return: a RewardSignalResult of (scaled intrinsic reward, unscaled intrinsic reward) provided by the generator
|
|
"""
|
|
mini_batch_len = len(next(iter(mini_batch.values())))
|
|
return RewardSignalResult(
|
|
self.strength * np.zeros(mini_batch_len, dtype=np.float32),
|
|
np.zeros(mini_batch_len, dtype=np.float32),
|
|
)
|
|
|
|
def prepare_update(
|
|
self, policy: TFPolicy, mini_batch: AgentBuffer, num_sequences: int
|
|
) -> Dict[tf.Tensor, Any]:
|
|
"""
|
|
If the reward signal has an internal model (e.g. GAIL or Curiosity), get the feed_dict
|
|
needed to update the buffer..
|
|
:param update_buffer: An AgentBuffer that contains the live data from which to update.
|
|
:param n_sequences: The number of sequences in the training buffer.
|
|
:return: A dict that corresponds to the feed_dict needed for the update.
|
|
"""
|
|
return {}
|
|
|
|
@classmethod
|
|
def check_config(
|
|
cls, config_dict: Dict[str, Any], param_keys: List[str] = None
|
|
) -> None:
|
|
"""
|
|
Check the config dict, and throw an error if there are missing hyperparameters.
|
|
"""
|
|
param_keys = param_keys or []
|
|
for k in param_keys:
|
|
if k not in config_dict:
|
|
raise UnityTrainerException(
|
|
"The hyper-parameter {0} could not be found for {1}.".format(
|
|
k, cls.__name__
|
|
)
|
|
)
|