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