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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3.9 KiB

import pathlib
import logging
import os
from mlagents.trainers.buffer import Buffer
from mlagents.envs.brain import BrainParameters, BrainInfo
from mlagents.envs.communicator_objects import *
from google.protobuf.internal.decoder import _DecodeVarint32
logger = logging.getLogger("mlagents.trainers")
def make_demo_buffer(brain_infos, brain_params, sequence_length):
# Create and populate buffer using experiences
demo_buffer = Buffer()
for idx, experience in enumerate(brain_infos):
if idx > len(brain_infos) - 2:
break
current_brain_info = brain_infos[idx]
next_brain_info = brain_infos[idx + 1]
demo_buffer[0].last_brain_info = current_brain_info
demo_buffer[0]['done'].append(next_brain_info.local_done[0])
demo_buffer[0]['rewards'].append(next_brain_info.rewards[0])
for i in range(brain_params.number_visual_observations):
demo_buffer[0]['visual_obs%d' % i] \
.append(current_brain_info.visual_observations[i][0])
if brain_params.vector_observation_space_size > 0:
demo_buffer[0]['vector_obs'] \
.append(current_brain_info.vector_observations[0])
demo_buffer[0]['actions'].append(next_brain_info.previous_vector_actions[0])
if next_brain_info.local_done[0]:
demo_buffer.append_update_buffer(0, batch_size=None,
training_length=sequence_length)
demo_buffer.reset_local_buffers()
demo_buffer.append_update_buffer(0, batch_size=None,
training_length=sequence_length)
return demo_buffer
def demo_to_buffer(file_path, sequence_length):
"""
Loads demonstration file and uses it to fill training buffer.
:param file_path: Location of demonstration file (.demo).
:param sequence_length: Length of trajectories to fill buffer.
:return:
"""
brain_params, brain_infos, _ = load_demonstration(file_path)
demo_buffer = make_demo_buffer(brain_infos, brain_params, sequence_length)
return brain_params, demo_buffer
def load_demonstration(file_path):
"""
Loads and parses a demonstration file.
:param file_path: Location of demonstration file (.demo).
:return: BrainParameter and list of BrainInfos containing demonstration data.
"""
# First 32 bytes of file dedicated to meta-data.
INITIAL_POS = 33
if not os.path.isfile(file_path):
raise FileNotFoundError("The demonstration file {} does not exist.".format(file_path))
file_extension = pathlib.Path(file_path).suffix
if file_extension != '.demo':
raise ValueError("The file is not a '.demo' file. Please provide a file with the "
"correct extension.")
brain_params = None
brain_infos = []
data = open(file_path, "rb").read()
next_pos, pos, obs_decoded = 0, 0, 0
total_expected = 0
while pos < len(data):
next_pos, pos = _DecodeVarint32(data, pos)
if obs_decoded == 0:
meta_data_proto = DemonstrationMetaProto()
meta_data_proto.ParseFromString(data[pos:pos + next_pos])
total_expected = meta_data_proto.number_steps
pos = INITIAL_POS
if obs_decoded == 1:
brain_param_proto = BrainParametersProto()
brain_param_proto.ParseFromString(data[pos:pos + next_pos])
brain_params = BrainParameters.from_proto(brain_param_proto)
pos += next_pos
if obs_decoded > 1:
agent_info = AgentInfoProto()
agent_info.ParseFromString(data[pos:pos + next_pos])
brain_info = BrainInfo.from_agent_proto([agent_info], brain_params)
brain_infos.append(brain_info)
if len(brain_infos) == total_expected:
break
pos += next_pos
obs_decoded += 1
return brain_params, brain_infos, total_expected