Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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58 行
2.7 KiB

from typing import Dict
class BrainInfo:
def __init__(self, visual_observation, vector_observation, text_observations, memory=None,
reward=None, agents=None, local_done=None,
vector_action=None, text_action=None, max_reached=None):
"""
Describes experience at current step of all agents linked to a brain.
"""
self.visual_observations = visual_observation
self.vector_observations = vector_observation
self.text_observations = text_observations
self.memories = memory
self.rewards = reward
self.local_done = local_done
self.max_reached = max_reached
self.agents = agents
self.previous_vector_actions = vector_action
self.previous_text_actions = text_action
AllBrainInfo = Dict[str, BrainInfo]
class BrainParameters:
def __init__(self, brain_name, brain_param):
"""
Contains all brain-specific parameters.
:param brain_name: Name of brain.
:param brain_param: Dictionary of brain parameters.
"""
self.brain_name = brain_name
self.vector_observation_space_size = brain_param["vectorObservationSize"]
self.num_stacked_vector_observations = brain_param["numStackedVectorObservations"]
self.number_visual_observations = len(brain_param["cameraResolutions"])
self.camera_resolutions = brain_param["cameraResolutions"]
self.vector_action_space_size = brain_param["vectorActionSize"]
self.vector_action_descriptions = brain_param["vectorActionDescriptions"]
self.vector_action_space_type = ["discrete", "continuous"][brain_param["vectorActionSpaceType"]]
self.vector_observation_space_type = ["discrete", "continuous"][brain_param["vectorObservationSpaceType"]]
def __str__(self):
return '''Unity brain name: {0}
Number of Visual Observations (per agent): {1}
Vector Observation space type: {2}
Vector Observation space size (per agent): {3}
Number of stacked Vector Observation: {4}
Vector Action space type: {5}
Vector Action space size (per agent): {6}
Vector Action descriptions: {7}'''.format(self.brain_name,
str(self.number_visual_observations),
self.vector_observation_space_type,
str(self.vector_observation_space_size),
str(self.num_stacked_vector_observations),
self.vector_action_space_type,
str(self.vector_action_space_size),
', '.join(self.vector_action_descriptions))