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
98 行
3.5 KiB
98 行
3.5 KiB
from mlagents.envs.communicator import Communicator
|
|
from mlagents.envs.communicator_objects import UnityMessage, UnityOutput, UnityInput, \
|
|
ResolutionProto, BrainParametersProto, UnityRLInitializationOutput, \
|
|
AgentInfoProto, UnityRLOutput
|
|
|
|
|
|
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: UnityInput) -> UnityOutput:
|
|
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 = UnityRLInitializationOutput(
|
|
name="RealFakeAcademy",
|
|
version="API-7",
|
|
log_path="",
|
|
brain_parameters=[bp]
|
|
)
|
|
return UnityOutput(
|
|
rl_initialization_output=rl_init
|
|
)
|
|
|
|
def exchange(self, inputs: UnityInput) -> UnityOutput:
|
|
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"] = \
|
|
UnityRLOutput.ListAgentInfoProto(value=list_agent_info)
|
|
global_done = False
|
|
try:
|
|
fake_brain = inputs.rl_input.agent_actions["RealFakeBrain"]
|
|
global_done = (fake_brain.value[0].vector_actions[0] == -1)
|
|
except:
|
|
pass
|
|
result = UnityRLOutput(
|
|
global_done=global_done,
|
|
agentInfos=dict_agent_info
|
|
)
|
|
return UnityOutput(
|
|
rl_output=result
|
|
)
|
|
|
|
def close(self):
|
|
"""
|
|
Sends a shutdown signal to the unity environment, and closes the grpc connection.
|
|
"""
|
|
self.has_been_closed = True
|