Add load & save function.

Add load & save function.
Add train flag to test model.
Add new action select function while in test mode.
Add decision period to skip step.

.gitignore

@@ -81,8 +81,6 @@ crashlytics-build.properties
 /Aimbot-PPO-Python/Pytorch/runs/
 /Aimbot-PPO-Python/Pytorch/wandb/
 /Aimbot-PPO-Python/Backup/
-/Aimbot-PPO-Python/Build-MultiScene-WithLoad/
+/Aimbot-PPO-Python/Build/
-/Aimbot-PPO-Python/Build-CloseEnemyCut/
-/Aimbot-PPO-Python/Build-ParallelEnv/
 /Aimbot-PPO-Python/PPO-Model/
 /Aimbot-PPO-Python/GAIL-Expert-Data/

Aimbot-PPO-Python/Pytorch/ppo.py

@@ -13,30 +13,36 @@ from torch.distributions.categorical import Categorical
 from distutils.util import strtobool
 from torch.utils.tensorboard import SummaryWriter
 
+bestReward = 0
+
 DEFAULT_SEED = 9331
-ENV_PATH = "../Build-ParallelEnv/Aimbot-ParallelEnv"
+ENV_PATH = "../Build/Build-ParallelEnv-BigArea-6Enemy/Aimbot-ParallelEnv"
 WAND_ENTITY = "koha9"
 WORKER_ID = 1
-BASE_PORT = 2002
+BASE_PORT = 1000
 
 
+TOTAL_STEPS = 2000000
+STEP_NUM = 314
+DECISION_PERIOD = 2
 LEARNING_RATE = 7e-4
 GAMMA = 0.99
 GAE_LAMBDA = 0.95
-TOTAL_STEPS = 2000000
+MINIBATCH_NUM = 4
-STEP_NUM = 256
-MINIBATCH_NUM = 1
 EPOCHS = 4
 CLIP_COEF = 0.1
+POLICY_COEF = 1.0
 ENTROPY_COEF = 0.01
 CRITIC_COEF = 0.5
 
 ANNEAL_LEARNING_RATE = True
 CLIP_VLOSS = True
 NORM_ADV = True
+TRAIN = True
 
-WANDB_TACK = True
+WANDB_TACK = False
-LOAD_DIR = "../PPO-Model/SmallArea-256-128-hybrid.pt"
+LOAD_DIR = None
+# LOAD_DIR = "../PPO-Model/SmallArea-256-128-hybrid-2nd-trainning.pt"
 
 
 def parse_args():
@@ -59,6 +65,8 @@ def parse_args():
                         help="total timesteps of the experiments")
 
     # model parameters
+    parser.add_argument("--train",type=lambda x: bool(strtobool(x)), default=TRAIN, nargs="?", const=True,
+                        help="Train Model or not")
     parser.add_argument("--stepNum", type=int, default=STEP_NUM,
                         help="the number of steps to run in each environment per policy rollout")
     parser.add_argument("--minibatchesNum", type=int, default=MINIBATCH_NUM,
@@ -73,8 +81,10 @@ def parse_args():
                         help="the entity (team) of wandb's project")
     parser.add_argument("--load-dir", type=str, default=LOAD_DIR,
                         help="load model directory")
+    parser.add_argument("--decision-period", type=int, default=DECISION_PERIOD,
+                        help="the number of steps to run in each environment per policy rollout")
 
-    # GAE
+    # GAE loss
     parser.add_argument("--gae", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
                         help="Use GAE for advantage computation")
     parser.add_argument("--norm-adv", type=lambda x: bool(strtobool(x)), default=NORM_ADV, nargs="?", const=True,
@@ -85,6 +95,8 @@ def parse_args():
                         help="the lambda for the general advantage estimation")
     parser.add_argument("--clip-coef", type=float, default=CLIP_COEF,
                         help="the surrogate clipping coefficient")
+    parser.add_argument("--policy-coef", type=float, default=POLICY_COEF,
+                        help="coefficient of the policy")
     parser.add_argument("--ent-coef", type=float, default=ENTROPY_COEF,
                         help="coefficient of the entropy")
     parser.add_argument("--critic-coef", type=float, default=CRITIC_COEF,
@@ -114,15 +126,15 @@ class PPOAgent(nn.Module):
         self.continuous_size = env.unity_continuous_size
 
         self.network = nn.Sequential(
-            layer_init(nn.Linear(np.array(env.unity_observation_shape).prod(), 256)),
+            layer_init(nn.Linear(np.array(env.unity_observation_shape).prod(), 384)),
             nn.ReLU(),
-            layer_init(nn.Linear(256, 128)),
+            layer_init(nn.Linear(384, 256)),
             nn.ReLU(),
         )
-        self.actor_dis = layer_init(nn.Linear(128, self.discrete_size), std=0.01)
+        self.actor_dis = layer_init(nn.Linear(256, self.discrete_size), std=0.01)
-        self.actor_mean = layer_init(nn.Linear(128, self.continuous_size), std=0.01)
+        self.actor_mean = layer_init(nn.Linear(256, self.continuous_size), std=0.01)
         self.actor_logstd = nn.Parameter(torch.zeros(1, self.continuous_size))
-        self.critic = layer_init(nn.Linear(128, 1), std=1)
+        self.critic = layer_init(nn.Linear(256, 1), std=1)
 
     def get_value(self, state: torch.Tensor):
         return self.critic(self.network(state))
@@ -140,9 +152,16 @@ class PPOAgent(nn.Module):
         con_probs = Normal(actions_mean, action_std)
 
         if actions is None:
+            if args.train:
+                # select actions base on probability distribution model
                 disAct = torch.stack([ctgr.sample() for ctgr in multi_categoricals])
                 conAct = con_probs.sample()
                 actions = torch.cat([disAct.T, conAct], dim=1)
+            else:
+                # select actions base on best probability distribution
+                disAct = torch.stack([torch.argmax(logit, dim=1) for logit in split_logits])
+                conAct = actions_mean
+                actions = torch.cat([disAct.T, conAct], dim=1)
         else:
             disAct = actions[:, 0 : env.unity_discrete_type].T
             conAct = actions[:, env.unity_discrete_type :]
@@ -181,7 +200,7 @@ if __name__ == "__main__":
 
     # Tensorboard and WandB Recorder
     game_name = "Aimbot"
-    run_name = f"{game_name}__{args.seed}__{int(time.time())}"
+    run_name = f"{game_name}_{args.seed}_{int(time.time())}"
     if args.wandb_track:
         wandb.init(
             project=run_name,
@@ -227,14 +246,19 @@ if __name__ == "__main__":
             optimizer.param_groups[0]["lr"] = lrnow
 
         # MAIN LOOP: run agent in environment
-        for step in range(args.stepNum):
+        for i in range(args.stepNum * args.decision_period):
+            if i % args.decision_period == 0:
+                step = round(i / args.decision_period)
+                # Choose action by agent
                 global_step += 1 * env.unity_agent_num
                 obs[step] = next_obs
                 dones[step] = next_done
 
                 with torch.no_grad():
                     # predict actions
-                action, dis_logprob, _, con_logprob, _, value = agent.get_actions_value(next_obs)
+                    action, dis_logprob, _, con_logprob, _, value = agent.get_actions_value(
+                        next_obs
+                    )
                     value = value.flatten()
                 next_obs, reward, done = env.step(action.cpu().numpy())
 
@@ -244,7 +268,15 @@ if __name__ == "__main__":
                 con_logprobs[step] = con_logprob
                 values[step] = value
                 rewards[step] = torch.tensor(reward).to(device).view(-1)
-            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
+                next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(
+                    device
+                )
+            else:
+                # skip this step use last predict action
+                next_obs, reward, done = env.step(action.cpu().numpy())
+                next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(
+                    device
+                )
 
         # GAE
         with torch.no_grad():
@@ -276,6 +308,7 @@ if __name__ == "__main__":
                     returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
                 advantages = returns - values
 
+        if args.train:
             # flatten the batch
             b_obs = obs.reshape((-1,) + env.unity_observation_shape)
             b_dis_logprobs = dis_logprobs.reshape(-1)
@@ -287,7 +320,7 @@ if __name__ == "__main__":
 
             # Optimizing the policy and value network
             b_inds = np.arange(args.batch_size)
-        #clipfracs = []
+            # clipfracs = []
             for epoch in range(args.epochs):
                 # shuffle all datasets
                 np.random.shuffle(b_inds)
@@ -357,8 +390,8 @@ if __name__ == "__main__":
                     # total loss
                     entropy_loss = dis_entropy.mean() + con_entropy.mean()
                     loss = (
-                    dis_pg_loss
+                        dis_pg_loss * args.policy_coef
-                    + con_pg_loss
+                        + con_pg_loss * args.policy_coef
                         - entropy_loss * args.ent_coef
                         + v_loss * args.critic_coef
                     )
@@ -375,6 +408,7 @@ if __name__ == "__main__":
                         break
                 """
             # record rewards for plotting purposes
+            rewardsMean = np.mean(rewards.to("cpu").detach().numpy().copy())
             writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
             writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
             writer.add_scalar("losses/dis_policy_loss", dis_pg_loss.item(), global_step)
@@ -383,12 +417,17 @@ if __name__ == "__main__":
             writer.add_scalar("losses/entropy_loss", entropy_loss.item(), global_step)
             # writer.add_scalar("losses/old_approx_kl", old_approx_kl.item(), global_step)
             # writer.add_scalar("losses/approx_kl", approx_kl.item(), global_step)
-        #writer.add_scalar("losses/clipfrac", np.mean(clipfracs), global_step)
+            # writer.add_scalar("losses/clipfrac", np.mean(clipfracs), global_step)
-        print("SPS:", int(global_step / (time.time() - start_time)))
+            # print("SPS:", int(global_step / (time.time() - start_time)))
-        writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step)
+            print("episode over mean reward:", rewardsMean)
             writer.add_scalar(
-            "charts/Reward", np.mean(rewards.to("cpu").detach().numpy().copy()), global_step
+                "charts/SPS", int(global_step / (time.time() - start_time)), global_step
             )
+            writer.add_scalar("charts/Reward", rewardsMean, global_step)
+            if rewardsMean > bestReward:
+                bestReward = rewardsMean
+                saveDir = "../PPO-Model/bigArea-384-128-hybrid-" + str(rewardsMean) + ".pt"
+                torch.save(agent, saveDir)
 
     env.close()
     writer.close()

Aimbot-PPO-Python/Pytorch/testarea.ipynb

@@ -431,6 +431,45 @@
     "mymodel = torch.load(\"../PPO-Model/SmallArea-256-128-hybrid.pt\")\n",
     "mymodel.eval()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "x : torch.Size([2, 3, 4])\n",
+      "x : torch.Size([6, 2, 3, 4])\n",
+      "x : torch.Size([6, 2, 3, 4])\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "#1\n",
+    "x = torch.randn(2, 1, 1)#为1可以扩展为3和4\n",
+    "x = x.expand(2, 3, 4)\n",
+    "print('x :', x.size())\n",
+    "\n",
+    "#2\n",
+    "#扩展一个新的维度必须在最前面，否则会报错\n",
+    "#x = x.expand(2, 3, 4, 6)\n",
+    "\n",
+    "x = x.expand(6, 2, 3, 4)\n",
+    "print('x :', x.size())\n",
+    "\n",
+    "#3\n",
+    "#某一个维度为-1表示不改变该维度的大小\n",
+    "x = x.expand(6, -1, -1, -1)\n",
+    "print('x :', x.size())\n",
+    "\n",
+    "x : torch.Size([2, 3, 4])\n",
+    "x : torch.Size([6, 2, 3, 4])\n",
+    "x : torch.Size([6, 2, 3, 4])"
+   ]
   }
  ],
  "metadata": {