将Tensor改为tensor

Tensor与tensor的问题，规范化tensor使用。

Aimbot-PPO-Python/Pytorch/AimbotEnv.py

@@ -11,7 +11,6 @@ from mlagents_envs.side_channel.side_channel import (
     IncomingMessage,
     OutgoingMessage,
 )
-from arguments import set_save_model
 
 
 class Aimbot(gym.Env):
@@ -177,21 +176,18 @@ class AimbotSideChannel(SideChannel):
         "Warning|Message1|Message2|Message3" or
         "Error|Message1|Message2|Message3"
         """
-        this_message_Original = msg.read_string()
+        this_message = msg.read_string()
-        this_message = this_message_Original.split("|")
+        this_result = this_message.split("|")
-        print(this_message)
+        print(this_result)
-        if this_message[0] == "Warning":
+        if this_result[0] == "Warning":
-            if this_message[1] == "Result":
+            if this_result[1] == "Result":
-                airecorder.total_rounds[this_message[2]] += 1
+                airecorder.total_rounds[this_result[2]] += 1
-                if this_message[3] == "Win":
+                if this_result[3] == "Win":
-                    airecorder.win_rounds[this_message[2]] += 1
+                    airecorder.win_rounds[this_result[2]] += 1
                 # print(TotalRounds)
                 # print(WinRounds)
-            if this_message[1] == "Command":
+        elif this_result[0] == "Error":
-                set_save_model(True)
+            print(this_message)
-                print("Command: " + this_message_Original)
-        elif this_message[0] == "Error":
-            print(this_message_Original)
         # # while Message type is Warning
         # if(thisResult[0] == "Warning"):
         #     # while Message1 is result means one game is over

Aimbot-PPO-Python/Pytorch/Archive/test2.ipynb

@@ -81,43 +81,130 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "import argparse\n",
+    "import wandb\n",
+    "import time\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import uuid\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "\n",
+    "from AimbotEnv import Aimbot\n",
+    "from tqdm import tqdm\n",
+    "from torch.distributions.normal import Normal\n",
+    "from torch.distributions.categorical import Categorical\n",
+    "from distutils.util import strtobool\n",
+    "from torch.utils.tensorboard import SummaryWriter\n",
+    "from mlagents_envs.environment import UnityEnvironment\n",
+    "from mlagents_envs.side_channel.side_channel import (\n",
+    "    SideChannel,\n",
+    "    IncomingMessage,\n",
+    "    OutgoingMessage,\n",
+    ")\n",
+    "from typing import List\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "ename": "AttributeError",
-     "output_type": "stream",
+     "evalue": "'aaa' object has no attribute 'outa'",
-     "text": [
+     "output_type": "error",
-      "0\n",
+     "traceback": [
-      "i =  0\n",
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "i =  1\n",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "i =  2\n",
+      "Cell \u001b[1;32mIn[5], line 14\u001b[0m\n\u001b[0;32m     12\u001b[0m asd \u001b[39m=\u001b[39m aaa(outa, outb)\n\u001b[0;32m     13\u001b[0m asd\u001b[39m.\u001b[39mfunc()\n\u001b[1;32m---> 14\u001b[0m \u001b[39mprint\u001b[39m(asd\u001b[39m.\u001b[39;49mouta)  \u001b[39m# 输出 100\u001b[39;00m\n",
-      "i =  3\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'aaa' object has no attribute 'outa'"
-      "i =  4\n",
-      "i =  5\n",
-      "i =  6\n",
-      "i =  7\n",
-      "i =  8\n",
-      "i =  9\n",
-      "10\n"
      ]
     }
    ],
    "source": [
-    "import threading\n",
+    "class aaa():\n",
+    "    def __init__(self, a, b):\n",
+    "        self.a = a\n",
+    "        self.b = b\n",
     "\n",
-    "num = 0\n",
+    "    def func(self):\n",
+    "        global outa\n",
+    "        outa = 100\n",
     "\n",
-    "def print_numers():\n",
+    "outa = 1\n",
-    "    global num\n",
+    "outb = 2\n",
-    "    for i in range(10):\n",
+    "asd = aaa(outa, outb)\n",
-    "        num +=1\n",
+    "asd.func()\n",
-    "        print(\"i = \",i)\n",
+    "print(asd.outa)  # 输出 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "usage: ipykernel_launcher.py [-h] [--seed SEED]\n",
+      "ipykernel_launcher.py: error: unrecognized arguments: --ip=127.0.0.1 --stdin=9003 --control=9001 --hb=9000 --Session.signature_scheme=\"hmac-sha256\" --Session.key=b\"46ef9317-59fb-4ab6-ae4e-6b35744fc423\" --shell=9002 --transport=\"tcp\" --iopub=9004 --f=c:\\Users\\UCUNI\\AppData\\Roaming\\jupyter\\runtime\\kernel-v2-311926K1uko38tdWb.json\n"
+     ]
+    },
+    {
+     "ename": "SystemExit",
+     "evalue": "2",
+     "output_type": "error",
+     "traceback": [
+      "An exception has occurred, use %tb to see the full traceback.\n",
+      "\u001b[1;31mSystemExit\u001b[0m\u001b[1;31m:\u001b[0m 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "import argparse\n",
     "\n",
-    "thread = threading.Thread(target=print_numers)\n",
+    "def parse_args():\n",
+    "    parser = argparse.ArgumentParser()\n",
+    "    parser.add_argument(\"--seed\", type=int, default=11,\n",
+    "                        help=\"seed of the experiment\")\n",
+    "    args = parser.parse_args()\n",
+    "    return args\n",
     "\n",
-    "print(num)\n",
+    "arggg = parse_args()\n",
-    "thread.start()\n",
+    "print(type(arggg))"
-    "thread.join()\n",
+   ]
-    "print(num)"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0.]])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "np.zeros((8, 4))"
    ]
   }
  ],

Aimbot-PPO-Python/Pytorch/MultiNN-PPO.py

@@ -4,7 +4,6 @@ import random
 import uuid
 import torch
 import atexit
-import os
 
 from aimbotEnv import Aimbot
 from aimbotEnv import AimbotSideChannel
@@ -13,14 +12,13 @@ from airecorder import WandbRecorder
 from aimemory import PPOMem
 from aimemory import Targets
 from arguments import parse_args
-from arguments import set_save_model, is_save_model
 import torch.optim as optim
 
 # side channel uuid
 SIDE_CHANNEL_UUID = uuid.UUID("8bbfb62a-99b4-457c-879d-b78b69066b5e")
 # tensorboard names
-GAME_NAME = "Aimbot_Hybrid_Full_MNN_MultiLevel_V2"
+GAME_NAME = "Aimbot_Hybrid_V3"
-GAME_TYPE = "GotoOnly-Level0123-new512Model"
+GAME_TYPE = "Mix_Verification"
 
 if __name__ == "__main__":
     args = parse_args()
@@ -49,8 +47,9 @@ if __name__ == "__main__":
         # freeze
         if args.freeze_viewnet:
             # freeze the view network
-            print("FREEZE VIEW NETWORK is not compatible with Full MNN!")
+            for p in agent.viewNetwork.parameters():
-            raise NotImplementedError
+                p.requires_grad = False
+            print("VIEW NETWORK FREEZE")
         print("Load Agent", args.load_dir)
         print(agent.eval())
     # optimizer
@@ -59,6 +58,16 @@ if __name__ == "__main__":
     run_name = f"{GAME_TYPE}_{args.seed}_{int(time.time())}"
     wdb_recorder = WandbRecorder(GAME_NAME, GAME_TYPE, run_name, args)
 
+    @atexit.register
+    def save_model():
+        # close env
+        env.close()
+        if args.save_model:
+            # save model while exit
+            save_dir = "../PPO-Model/" + run_name + "_last.pt"
+            torch.save(agent, save_dir)
+            print("save model to " + save_dir)
+
     # start the game
     total_update_step = args.target_num * args.total_timesteps // args.datasetSize
     target_steps = [0 for i in range(args.target_num)]
@@ -214,16 +223,11 @@ if __name__ == "__main__":
             )
             # print cost time as seconds
             print("cost time:", time.time() - start_time)
-            # New Record! or save model
+            # New Record!
-            if ((is_save_model() or TotalRewardMean > best_reward) and args.save_model):
+            if TotalRewardMean > best_reward and args.save_model:
-                # check saveDir is exist
+                best_reward = target_reward_mean
-                saveDir = "../PPO-Model/" + run_name + "/"
+                saveDir = "../PPO-Model/" + run_name + "_" + str(TotalRewardMean) + ".pt"
-                if not os.path.isdir(saveDir):
+                torch.save(agent, saveDir)
-                    os.mkdir(saveDir)
-                best_reward = TotalRewardMean
-                torch.save(agent, saveDir + str(TotalRewardMean) + ".pt")
-                print("Model Saved!")
-                set_save_model(False)
         else:
             # train mode off
             mean_reward_list = []  # for WANDB
@@ -246,10 +250,7 @@ if __name__ == "__main__":
             TotalRewardMean = np.mean(mean_reward_list)
             wdb_recorder.writer.add_scalar("GlobalCharts/TotalRewardMean", TotalRewardMean, total_steps)
 
-    saveDir = "../PPO-Model/" + run_name + "/"
+    saveDir = "../PPO-Model/" + run_name + "_last.pt"
-    if not os.path.isdir(saveDir):
+    torch.save(agent, saveDir)
-        os.mkdir(saveDir)
-    best_reward = target_reward_mean
-    torch.save(agent, saveDir + "_last.pt")
     env.close()
     wdb_recorder.writer.close()

Aimbot-PPO-Python/Pytorch/arguments.py

@@ -4,38 +4,41 @@ import uuid
 from distutils.util import strtobool
 
 DEFAULT_SEED = 9331
-ENV_PATH = "../Build/3.5/Aimbot-ParallelEnv"
+ENV_PATH = "../Build/3.1.6/Aimbot-ParallelEnv"
 WAND_ENTITY = "koha9"
 WORKER_ID = 1
 BASE_PORT = 1000
 
 # tensorboard names
+GAME_NAME = "Aimbot_Target_Hybrid_PMNN_V3"
+GAME_TYPE = "Mix_Verification"
 
 # max round steps per agent is 2500/Decision_period, 25 seconds
 TOTAL_STEPS = 3150000
 BATCH_SIZE = 512
 MAX_TRAINNING_DATASETS = 6000
 DECISION_PERIOD = 1
-LEARNING_RATE = 1.5e-4
+LEARNING_RATE = 6.5e-4
 GAMMA = 0.99
 GAE_LAMBDA = 0.95
 EPOCHS = 3
 CLIP_COEF = 0.11
 LOSS_COEF = [1.0, 1.0, 1.0, 1.0] # free go attack defence
-POLICY_COEF = [0.8, 0.8, 0.8, 0.8]
+POLICY_COEF = [1.0, 1.0, 1.0, 1.0]
 ENTROPY_COEF = [0.05, 0.05, 0.05, 0.05]
-CRITIC_COEF = [0.8, 0.8, 0.8, 0.8]
+CRITIC_COEF = [0.5, 0.5, 0.5, 0.5]
 TARGET_LEARNING_RATE = 1e-6
 
 FREEZE_VIEW_NETWORK = False
+BROADCASTREWARD = False
 ANNEAL_LEARNING_RATE = True
 CLIP_VLOSS = True
 NORM_ADV = False
 TRAIN = True
-SAVE_MODEL = True
+SAVE_MODEL = False
-WANDB_TACK = True
+WANDB_TACK = False
 LOAD_DIR = None
-# LOAD_DIR = "../PPO-Model/GotoOnly-Level0123_9331_1696965321/5.1035867.pt"
+#LOAD_DIR = "../PPO-Model/PList_Go_LeakyReLU_9331_1677965178_bestGoto/PList_Go_LeakyReLU_9331_1677965178_10.709002.pt"
 
 # Unity Environment Parameters
 TARGET_STATE_SIZE = 6
@@ -50,16 +53,6 @@ TARGETNUM= 4
 ENV_TIMELIMIT = 30
 RESULT_BROADCAST_RATIO = 1/ENV_TIMELIMIT
 
-save_model_this_episode = False
-
-def is_save_model():
-    global save_model_this_episode
-    return save_model_this_episode
-def set_save_model(save_model:bool):
-    print("set save model to ",save_model)
-    global save_model_this_episode
-    save_model_this_episode = save_model
-
 def parse_args():
     # fmt: off
     # pytorch and environment parameters
@@ -104,10 +97,12 @@ def parse_args():
                         help="the number of steps to run in each environment per policy rollout")
     parser.add_argument("--result-broadcast-ratio", type=float, default=RESULT_BROADCAST_RATIO,
                         help="broadcast result when win round is reached,r=result-broadcast-ratio*remainTime")
+    parser.add_argument("--broadCastEndReward", type=lambda x: bool(strtobool(x)), default=BROADCASTREWARD, nargs="?", const=True,
+                        help="save model or not")
     # target_learning_rate
     parser.add_argument("--target-lr", type=float, default=TARGET_LEARNING_RATE,
                         help="target value of downscaling the learning rate")
-
+    
     # POLICY_COEF ENTROPY_COEF CRITIC_COEF LOSS_COEF
     parser.add_argument("--policy-coef", type=float, default=POLICY_COEF,
                         help="coefficient of the policy loss")

Aimbot-PPO-Python/Pytorch/multiThread-PPO.py

@@ -1,255 +0,0 @@
-import time
-import numpy as np
-import random
-import uuid
-import torch
-import atexit
-import os
-
-from aimbotEnv import Aimbot
-from aimbotEnv import AimbotSideChannel
-from ppoagent import PPOAgent
-from airecorder import WandbRecorder
-from aimemory import PPOMem
-from aimemory import Targets
-from arguments import parse_args
-from arguments import set_save_model, is_save_model
-import torch.optim as optim
-
-# side channel uuid
-SIDE_CHANNEL_UUID = uuid.UUID("8bbfb62a-99b4-457c-879d-b78b69066b5e")
-# tensorboard names
-GAME_NAME = "Aimbot_Hybrid_Full_MNN_MultiLevel_V2"
-GAME_TYPE = "GotoOnly-Level0123-new512Model"
-
-if __name__ == "__main__":
-    args = parse_args()
-    random.seed(args.seed)
-    np.random.seed(args.seed)
-    torch.manual_seed(args.seed)
-
-    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
-    best_reward = -1
-
-    # Initialize environment agent optimizer
-    aimbot_side_channel = AimbotSideChannel(SIDE_CHANNEL_UUID)
-    env = Aimbot(
-        env_path=args.path,
-        worker_id=args.workerID,
-        base_port=args.baseport,
-        side_channels=[aimbot_side_channel])
-    if args.load_dir is None:
-        agent = PPOAgent(
-            env=env,
-            this_args=args,
-            device=device,
-        ).to(device)
-    else:
-        agent = torch.load(args.load_dir)
-        # freeze
-        if args.freeze_viewnet:
-            # freeze the view network
-            print("FREEZE VIEW NETWORK is not compatible with Full MNN!")
-            raise NotImplementedError
-        print("Load Agent", args.load_dir)
-        print(agent.eval())
-    # optimizer
-    optimizer = optim.Adam(agent.parameters(), lr=args.lr, eps=1e-5)
-    # Tensorboard and WandB Recorder
-    run_name = f"{GAME_TYPE}_{args.seed}_{int(time.time())}"
-    wdb_recorder = WandbRecorder(GAME_NAME, GAME_TYPE, run_name, args)
-
-    # start the game
-    total_update_step = args.target_num * args.total_timesteps // args.datasetSize
-    target_steps = [0 for i in range(args.target_num)]
-    start_time = time.time()
-    state, _, done = env.reset()
-
-    # initialize AI memories
-    ppo_memories = PPOMem(
-        args=args,
-        unity_agent_num=env.unity_agent_num,
-        device=device,
-    )
-
-    # MAIN LOOP: run agent in environment
-    for total_steps in range(total_update_step):
-        # discount learning rate, while step == total_update_step lr will be 0
-        if args.annealLR:
-            final_lr_ratio = args.target_lr / args.lr
-            frac = 1.0 - ((total_steps + 1.0) / total_update_step)
-            lr_now = frac * args.lr
-            optimizer.param_groups[0]["lr"] = lr_now
-        else:
-            lr_now = args.lr
-
-        # episode start show learning rate
-        print("new episode", total_steps, "learning rate = ", lr_now)
-        step = 0
-        training = False
-        train_queue = []
-        last_reward = [0. for i in range(env.unity_agent_num)]
-        # MAIN LOOP: run agent in environment
-        while True:
-            # Target Type(state[0][0]) is stay(4),use all zero action
-            if state[0][0] == 4:
-                next_state, reward, next_done = env.step(env.all_zero_action)
-                state, done = next_state, next_done
-                continue
-            # On decision point, and Target Type(state[0][0]) is not stay(4) choose action by agent
-            if step % args.decision_period == 0:
-                step += 1
-                # Choose action by agent
-                with torch.no_grad():
-                    # predict actions
-                    action, dis_logprob, _, con_logprob, _, value = agent.get_actions_value(
-                        torch.tensor(state,dtype=torch.float32).to(device)
-                    )
-                    value = value.flatten()
-
-                # variable from GPU to CPU
-                action_cpu = action.cpu().numpy()
-                dis_logprob_cpu = dis_logprob.cpu().numpy()
-                con_logprob_cpu = con_logprob.cpu().numpy()
-                value_cpu = value.cpu().numpy()
-                # Environment step
-                next_state, reward, next_done = env.step(action_cpu)
-
-                # save memories
-                if args.train:
-                    ppo_memories.save_memories(
-                        now_step=step,
-                        agent=agent,
-                        state=state,
-                        action_cpu=action_cpu,
-                        dis_logprob_cpu=dis_logprob_cpu,
-                        con_logprob_cpu=con_logprob_cpu,
-                        reward=reward,
-                        done=done,
-                        value_cpu=value_cpu,
-                        last_reward=last_reward,
-                        next_done=next_done,
-                        next_state=next_state,
-                    )
-                    # check if any training dataset is full and ready to train
-                    for i in range(args.target_num):
-                        if ppo_memories.obs[i].size()[0] >= args.datasetSize:
-                            # start train NN
-                            train_queue.append(i)
-                    if len(train_queue) > 0:
-                        # break while loop and start train
-                        break
-                    # update state
-                state, done = next_state, next_done
-            else:
-                step += 1
-                # skip this step use last predict action
-                next_state, reward, next_done = env.step(action_cpu)
-                # save memories
-                if args.train:
-                    ppo_memories.save_memories(
-                        now_step=step,
-                        agent=agent,
-                        state=state,
-                        action_cpu=action_cpu,
-                        dis_logprob_cpu=dis_logprob_cpu,
-                        con_logprob_cpu=con_logprob_cpu,
-                        reward=reward,
-                        done=done,
-                        value_cpu=value_cpu,
-                        last_reward=last_reward,
-                        next_done=next_done,
-                        next_state=next_state,
-                    )
-                    # update state
-                    state = next_state
-                    last_reward = reward
-
-        if args.train:
-            # train mode on
-            mean_reward_list = []  # for WANDB
-            # loop all training queue
-            for this_train_ind in train_queue:
-                # start time
-                start_time = time.time()
-                target_steps[this_train_ind] += 1
-                # train agent
-                (
-                    v_loss,
-                    dis_pg_loss,
-                    con_pg_loss,
-                    loss,
-                    entropy_loss
-                ) = agent.train_net(
-                    this_train_ind=this_train_ind,
-                    ppo_memories=ppo_memories,
-                    optimizer=optimizer
-                )
-                # record mean reward before clear history
-                print("done")
-                target_reward_mean = np.mean(ppo_memories.rewards[this_train_ind].to("cpu").detach().numpy().copy())
-                mean_reward_list.append(target_reward_mean)
-                targetName = Targets(this_train_ind).name
-
-                # clear this target training set buffer
-                ppo_memories.clear_training_datasets(this_train_ind)
-                # record rewards for plotting purposes
-                wdb_recorder.add_target_scalar(
-                    targetName,
-                    this_train_ind,
-                    v_loss,
-                    dis_pg_loss,
-                    con_pg_loss,
-                    loss,
-                    entropy_loss,
-                    target_reward_mean,
-                    target_steps,
-                )
-                print(f"episode over Target{targetName} mean reward:", target_reward_mean)
-            TotalRewardMean = np.mean(mean_reward_list)
-            wdb_recorder.add_global_scalar(
-                TotalRewardMean,
-                optimizer.param_groups[0]["lr"],
-                total_steps,
-            )
-            # print cost time as seconds
-            print("cost time:", time.time() - start_time)
-            # New Record! or save model
-            if ((is_save_model() or TotalRewardMean > best_reward) and args.save_model):
-                # check saveDir is exist
-                saveDir = "../PPO-Model/" + run_name + "/"
-                if not os.path.isdir(saveDir):
-                    os.mkdir(saveDir)
-                best_reward = TotalRewardMean
-                torch.save(agent, saveDir + str(TotalRewardMean) + ".pt")
-                print("Model Saved!")
-                set_save_model(False)
-        else:
-            # train mode off
-            mean_reward_list = []  # for WANDB
-            # while not in training mode, clear the buffer
-            for this_train_ind in train_queue:
-                target_steps[this_train_ind] += 1
-                targetName = Targets(this_train_ind).name
-                target_reward_mean = np.mean(ppo_memories.rewards[this_train_ind].to("cpu").detach().numpy().copy())
-                mean_reward_list.append(target_reward_mean)
-                print(target_steps[this_train_ind])
-
-                # clear this target training set buffer
-                ppo_memories.clear_training_datasets(this_train_ind)
-
-                # record rewards for plotting purposes
-                wdb_recorder.writer.add_scalar(f"Target{targetName}/Reward", target_reward_mean,
-                                               target_steps[this_train_ind])
-                wdb_recorder.add_win_ratio(targetName, target_steps[this_train_ind])
-                print(f"episode over Target{targetName} mean reward:", target_reward_mean)
-            TotalRewardMean = np.mean(mean_reward_list)
-            wdb_recorder.writer.add_scalar("GlobalCharts/TotalRewardMean", TotalRewardMean, total_steps)
-
-    saveDir = "../PPO-Model/" + run_name + "/"
-    if not os.path.isdir(saveDir):
-        os.mkdir(saveDir)
-    best_reward = target_reward_mean
-    torch.save(agent, saveDir + "_last.pt")
-    env.close()
-    wdb_recorder.writer.close()

Aimbot-PPO-Python/Pytorch/ppoagent.py

@@ -8,8 +8,6 @@ from aimbotEnv import Aimbot
 from torch.distributions.normal import Normal
 from torch.distributions.categorical import Categorical
 
-firstLayerNum = 512
-secondLayerNum = 128
 
 def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
     nn.init.orthogonal_(layer.weight, std)
@@ -48,70 +46,84 @@ class PPOAgent(nn.Module):
         self.discrete_shape = list(env.unity_discrete_branches)
         self.continuous_size = env.unity_continuous_size
 
-        self.hidden_networks = nn.ModuleList(
+        self.view_network = nn.Sequential(layer_init(nn.Linear(self.ray_state_size, 200)), nn.LeakyReLU())
+        self.target_networks = nn.ModuleList(
             [
-                nn.Sequential(
+                nn.Sequential(layer_init(nn.Linear(self.state_size_without_ray, 100)), nn.LeakyReLU())
-                    layer_init(nn.Linear(self.state_size, firstLayerNum)),
+                for i in range(self.target_num)
-                    nn.LeakyReLU(),
+            ]
-                    layer_init(nn.Linear(firstLayerNum, secondLayerNum)),
+        )
-                    nn.LeakyReLU(),
+        self.middle_networks = nn.ModuleList(
-                    )
+            [
+                nn.Sequential(layer_init(nn.Linear(300, 200)), nn.LeakyReLU())
                 for i in range(self.target_num)
             ]
         )
-
         self.actor_dis = nn.ModuleList(
-            [layer_init(nn.Linear(secondLayerNum, self.discrete_size), std=0.5) for i in range(self.target_num)]
+            [layer_init(nn.Linear(200, self.discrete_size), std=0.5) for i in range(self.target_num)]
         )
         self.actor_mean = nn.ModuleList(
-            [layer_init(nn.Linear(secondLayerNum, self.continuous_size), std=0) for i in range(self.target_num)]
+            [layer_init(nn.Linear(200, self.continuous_size), std=0.5) for i in range(self.target_num)]
         )
         self.actor_logstd = nn.ParameterList(
             [nn.Parameter(torch.zeros(1, self.continuous_size)) for i in range(self.target_num)]
-        )
+        )  # nn.Parameter(torch.zeros(1, self.continuous_size))
         self.critic = nn.ModuleList(
-            [layer_init(nn.Linear(secondLayerNum, 1), std=0) for i in range(self.target_num)]
+            [layer_init(nn.Linear(200, 1), std=1) for i in range(self.target_num)]
         )
 
     def get_value(self, state: torch.Tensor):
-        # get critic value
-        # state.size()[0] is batch_size
         target = state[:, 0].to(torch.int32)  # int
-        hidden_output = torch.stack(
+        this_state_num = target.size()[0]
-            [self.hidden_networks[target[i]](state[i]) for i in range(state.size()[0])]
+        view_input = state[:, -self.ray_state_size:]  # all ray input
+        target_input = state[:, : self.state_size_without_ray]
+        view_layer = self.view_network(view_input)
+        target_layer = torch.stack(
+            [self.target_networks[target[i]](target_input[i]) for i in range(this_state_num)]
+        )
+        middle_input = torch.cat([view_layer, target_layer], dim=1)
+        middle_layer = torch.stack(
+            [self.middle_networks[target[i]](middle_input[i]) for i in range(this_state_num)]
         )
         criticV = torch.stack(
-            [self.critic[target[i]](hidden_output[i]) for i in range(state.size()[0])]
+            [self.critic[target[i]](middle_layer[i]) for i in range(this_state_num)]
-        )
+        )  # self.critic
         return criticV
 
     def get_actions_value(self, state: torch.Tensor, actions=None):
-        # get actions and value
         target = state[:, 0].to(torch.int32)  # int
-        hidden_output = torch.stack(
+        this_state_num = target.size()[0]
-            [self.hidden_networks[target[i]](state[i]) for i in range(target.size()[0])]
+        view_input = state[:, -self.ray_state_size:]  # all ray input
+        target_input = state[:, : self.state_size_without_ray]
+        view_layer = self.view_network(view_input)
+        target_layer = torch.stack(
+            [self.target_networks[target[i]](target_input[i]) for i in range(this_state_num)]
+        )
+        middle_input = torch.cat([view_layer, target_layer], dim=1)
+        middle_layer = torch.stack(
+            [self.middle_networks[target[i]](middle_input[i]) for i in range(this_state_num)]
         )
 
         # discrete
         # 递归targets的数量,既agent数来实现根据target不同来选用对应的输出网络计算输出
         dis_logits = torch.stack(
-            [self.actor_dis[target[i]](hidden_output[i]) for i in range(target.size()[0])]
+            [self.actor_dis[target[i]](middle_layer[i]) for i in range(this_state_num)]
         )
         split_logits = torch.split(dis_logits, self.discrete_shape, dim=1)
         multi_categoricals = [Categorical(logits=thisLogits) for thisLogits in split_logits]
         # continuous
         actions_mean = torch.stack(
-            [self.actor_mean[target[i]](hidden_output[i]) for i in range(target.size()[0])]
+            [self.actor_mean[target[i]](middle_layer[i]) for i in range(this_state_num)]
         )  # self.actor_mean(hidden)
         action_logstd = torch.stack(
-            [torch.squeeze(self.actor_logstd[target[i]], 0) for i in range(target.size()[0])]
+            [torch.squeeze(self.actor_logstd[target[i]], 0) for i in range(this_state_num)]
         )
         # print(action_logstd)
         action_std = torch.exp(action_logstd)  # torch.exp(action_logstd)
         con_probs = Normal(actions_mean, action_std)
         # critic
         criticV = torch.stack(
-            [self.critic[target[i]](hidden_output[i]) for i in range(target.size()[0])]
+            [self.critic[target[i]](middle_layer[i]) for i in range(this_state_num)]
         )  # self.critic
 
         if actions is None: