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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3.0 KiB

from unityagents.communicator import Communicator
from communicator_objects import UnityMessage, UnityOutput, UnityInput,\
ResolutionProto, BrainParametersProto, UnityRLInitializationOutput,\
AgentInfoProto, UnityRLOutput
class MockCommunicator(Communicator):
def __init__(self, discrete_action=False, visual_inputs=0):
"""
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
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=3,
num_stacked_vector_observations=2,
vector_action_size=2,
camera_resolutions=resolutions,
vector_action_descriptions=["", ""],
vector_action_space_type=int(not self.is_discrete),
vector_observation_space_type=1,
brain_name="RealFakeBrain",
brain_type=2
)
rl_init = UnityRLInitializationOutput(
name="RealFakeAcademy",
version="API-4",
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 = []
for i in range(3):
list_agent_info.append(
AgentInfoProto(
stacked_vector_observation=[1, 2, 3, 1, 2, 3],
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:
global_done = (inputs.rl_input.agent_actions["RealFakeBrain"].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