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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from .communicator import Communicator
from mlagents.envs.communicator_objects.unity_rl_output_pb2 import UnityRLOutputProto
from mlagents.envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto
from mlagents.envs.communicator_objects.unity_rl_initialization_output_pb2 import (
UnityRLInitializationOutputProto,
)
from mlagents.envs.communicator_objects.unity_input_pb2 import UnityInputProto
from mlagents.envs.communicator_objects.unity_output_pb2 import UnityOutputProto
from mlagents.envs.communicator_objects.resolution_pb2 import ResolutionProto
from mlagents.envs.communicator_objects.agent_info_pb2 import AgentInfoProto
class MockCommunicator(Communicator):
def __init__(
self,
discrete_action=False,
visual_inputs=0,
stack=True,
num_agents=3,
brain_name="RealFakeBrain",
vec_obs_size=3,
):
"""
Python side of the grpc communication. Python is the client and Unity the server
:int base_port: Baseline port number to connect to Unity environment over. worker_id increments over this.
:int worker_id: Number to add to communication port (5005) [0]. Used for asynchronous agent scenarios.
"""
self.is_discrete = discrete_action
self.steps = 0
self.visual_inputs = visual_inputs
self.has_been_closed = False
self.num_agents = num_agents
self.brain_name = brain_name
self.vec_obs_size = vec_obs_size
if stack:
self.num_stacks = 2
else:
self.num_stacks = 1
def initialize(self, inputs: UnityInputProto) -> UnityOutputProto:
resolutions = [
ResolutionProto(width=30, height=40, gray_scale=False)
for i in range(self.visual_inputs)
]
bp = BrainParametersProto(
vector_observation_size=self.vec_obs_size,
num_stacked_vector_observations=self.num_stacks,
vector_action_size=[2],
camera_resolutions=resolutions,
vector_action_descriptions=["", ""],
vector_action_space_type=int(not self.is_discrete),
brain_name=self.brain_name,
is_training=True,
)
rl_init = UnityRLInitializationOutputProto(
name="RealFakeAcademy", version="API-10", log_path="", brain_parameters=[bp]
)
return UnityOutputProto(rl_initialization_output=rl_init)
def exchange(self, inputs: UnityInputProto) -> UnityOutputProto:
dict_agent_info = {}
if self.is_discrete:
vector_action = [1]
else:
vector_action = [1, 2]
list_agent_info = []
if self.num_stacks == 1:
observation = [1, 2, 3]
else:
observation = [1, 2, 3, 1, 2, 3]
for i in range(self.num_agents):
list_agent_info.append(
AgentInfoProto(
stacked_vector_observation=observation,
reward=1,
stored_vector_actions=vector_action,
stored_text_actions="",
text_observation="",
memories=[],
done=(i == 2),
max_step_reached=False,
id=i,
)
)
dict_agent_info["RealFakeBrain"] = UnityRLOutputProto.ListAgentInfoProto(
value=list_agent_info
)
result = UnityRLOutputProto(agentInfos=dict_agent_info)
return UnityOutputProto(rl_output=result)
def close(self):
"""
Sends a shutdown signal to the unity environment, and closes the grpc connection.
"""
self.has_been_closed = True