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
103 行
3.4 KiB
103 行
3.4 KiB
from .communicator import Communicator
|
|
from .communicator_objects import (
|
|
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-8", 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 Exception:
|
|
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
|