Side Channel Added

add side Channel to save target win ratio.

Aimbot-PPO-Python/Pytorch/AimbotEnv.py

@@ -12,12 +12,13 @@ class Aimbot(gym.Env):
         envPath: str,
         workerID: int = 1,
         basePort: int = 100,
+        side_channels: list = []
     ):
         super(Aimbot, self).__init__()
         self.env = UnityEnvironment(
             file_name=envPath,
             seed=1,
-            side_channels=[],
+            side_channels=side_channels,
             worker_id=workerID,
             base_port=basePort,
         )

Aimbot-PPO-Python/Pytorch/ppo.py

@@ -3,6 +3,7 @@ import wandb
 import time
 import numpy as np
 import random
+import uuid
 import torch
 import torch.nn as nn
 import torch.optim as optim
@@ -12,15 +13,24 @@ from torch.distributions.normal import Normal
 from torch.distributions.categorical import Categorical
 from distutils.util import strtobool
 from torch.utils.tensorboard import SummaryWriter
+from mlagents_envs.environment import UnityEnvironment
+from mlagents_envs.side_channel.side_channel import (
+    SideChannel,
+    IncomingMessage,
+    OutgoingMessage,
+)
+from typing import List
 
 bestReward = 0
 
 DEFAULT_SEED = 9331
-ENV_PATH = "../Build/Build-ParallelEnv-BigArea-6Enemy/Aimbot-ParallelEnv"
+ENV_PATH = "../Build/Build-ParallelEnv-Target-OffPolicy-SingleStack-SideChannel/Aimbot-ParallelEnv"
+SIDE_CHANNEL_UUID = uuid.UUID("8bbfb62a-99b4-457c-879d-b78b69066b5e")
 WAND_ENTITY = "koha9"
 WORKER_ID = 1
 BASE_PORT = 1000
 
+# !!!check every parameters before run!!!
 
 TOTAL_STEPS = 2000000
 STEP_NUM = 314
@@ -44,6 +54,10 @@ WANDB_TACK = False
 LOAD_DIR = None
 # LOAD_DIR = "../PPO-Model/SmallArea-256-128-hybrid-2nd-trainning.pt"
 
+# public data
+TotalRounds = {"Go":0,"Attack":0,"Free":0}
+WinRounds = {"Go":0,"Attack":0,"Free":0}
+
 
 def parse_args():
     # fmt: off
@@ -178,6 +192,51 @@ class PPOAgent(nn.Module):
             self.critic(hidden),
         )
 
+class AimbotSideChannel(SideChannel):
+    def __init__(self, channel_id: uuid.UUID) -> None:
+        super().__init__(channel_id)
+    def on_message_received(self, msg: IncomingMessage) -> None:
+        """
+        Note: We must implement this method of the SideChannel interface to
+        receive messages from Unity
+        """
+        thisMessage = msg.read_string()
+        print(thisMessage)
+        thisResult = thisMessage.split("|")
+        if(thisResult[0] == "result"):
+            TotalRounds[thisResult[1]]+=1
+            if(thisResult[2] == "Win"):
+                WinRounds[thisResult[1]]+=1
+            print(TotalRounds)
+            print(WinRounds)
+        elif(thisResult[0] == "Error"):
+            print(thisMessage)
+	# 发送函数
+    def send_string(self, data: str) -> None:
+        """发送一个字符串给C#"""
+        msg = OutgoingMessage()
+        msg.write_string(data)
+        super().queue_message_to_send(msg)
+
+    def send_bool(self, data: bool) -> None:
+        msg = OutgoingMessage()
+        msg.write_bool(data)
+        super().queue_message_to_send(msg)
+
+    def send_int(self, data: int) -> None:
+        msg = OutgoingMessage()
+        msg.write_int32(data)
+        super().queue_message_to_send(msg)
+
+    def send_float(self, data: float) -> None:
+        msg = OutgoingMessage()
+        msg.write_float32(data)
+        super().queue_message_to_send(msg)
+
+    def send_float_list(self, data: List[float]) -> None:
+        msg = OutgoingMessage()
+        msg.write_float32_list(data)
+        super().queue_message_to_send(msg)
 
 if __name__ == "__main__":
     args = parse_args()
@@ -188,7 +247,8 @@ if __name__ == "__main__":
     device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
 
     # Initialize environment anget optimizer
-    env = Aimbot(envPath=args.path, workerID=args.workerID, basePort=args.baseport)
+    aimBotsideChannel = AimbotSideChannel(SIDE_CHANNEL_UUID);
+    env = Aimbot(envPath=args.path, workerID=args.workerID, basePort=args.baseport,side_channels=[aimBotsideChannel])
     if args.load_dir is None:
         agent = PPOAgent(env).to(device)
     else:
@@ -424,6 +484,9 @@ if __name__ == "__main__":
                 "charts/SPS", int(global_step / (time.time() - start_time)), global_step
             )
             writer.add_scalar("charts/Reward", rewardsMean, global_step)
+            writer.add_scalar("charts/GoWinRatio", WinRounds["Go"]/TotalRounds["Go"] if TotalRounds["Go"] != 0 else 0, global_step)
+            writer.add_scalar("charts/AttackWinRatio", WinRounds["Attack"]/TotalRounds["Attack"] if TotalRounds["Attack"] != 0 else 0, global_step)
+            writer.add_scalar("charts/FreeWinRatio", WinRounds["Free"]/TotalRounds["Free"] if TotalRounds["Free"] != 0 else 0, global_step)
             if rewardsMean > bestReward:
                 bestReward = rewardsMean
                 saveDir = "../PPO-Model/bigArea-384-128-hybrid-" + str(rewardsMean) + ".pt"