improve train speed and add speed constrain

This reverts commit 648cf32e9c.

communication/server.py

@@ -72,37 +72,37 @@ class Server:
         self.commit(msg)
         self.send()
 
-    def receive(self) -> None:
-        """
-        Receive the next message from the TCP/IP socket and updates world
-        """
+    def receive(self):
 
-        # Receive message length information
-        if (
-            self.__socket.recv_into(
-                self.__rcv_buffer, nbytes=4, flags=socket.MSG_WAITALL
+        while True:
+
+            if (
+                    self.__socket.recv_into(
+                        self.__rcv_buffer, nbytes=4, flags=socket.MSG_WAITALL
+                    ) != 4
+            ):
+                raise ConnectionResetError
+
+            msg_size = int.from_bytes(self.__rcv_buffer[:4], byteorder="big", signed=False)
+
+            if msg_size > self.__rcv_buffer_size:
+                self.__rcv_buffer_size = msg_size
+                self.__rcv_buffer = bytearray(self.__rcv_buffer_size)
+
+            if (
+                    self.__socket.recv_into(
+                        self.__rcv_buffer, nbytes=msg_size, flags=socket.MSG_WAITALL
+                    ) != msg_size
+            ):
+                raise ConnectionResetError
+
+            self.world_parser.parse(
+                message=self.__rcv_buffer[:msg_size].decode()
             )
-            != 4
-        ):
-            raise ConnectionResetError
 
-        msg_size = int.from_bytes(self.__rcv_buffer[:4], byteorder="big", signed=False)
-
-        # Ensure receive buffer is large enough to hold the message
-        if msg_size > self.__rcv_buffer_size:
-            self.__rcv_buffer_size = msg_size
-            self.__rcv_buffer = bytearray(self.__rcv_buffer_size)
-
-        # Receive message with the specified length
-        if (
-            self.__socket.recv_into(
-                self.__rcv_buffer, nbytes=msg_size, flags=socket.MSG_WAITALL
-            )
-            != msg_size
-        ):
-            raise ConnectionResetError
-
-        self.world_parser.parse(message=self.__rcv_buffer[:msg_size].decode())
+            # 如果socket没有更多数据就退出
+            if len(select([self.__socket], [], [], 0.0)[0]) == 0:
+                break
 
     def commit_beam(self, pos2d: list, rotation: float) -> None:
         assert len(pos2d) == 2

scripts/commons/Server.py

@@ -18,29 +18,38 @@ class Server():
         # makes it easier to kill test servers without affecting train servers
         cmd = "rcssservermj"
         for i in range(n_servers):
+            port = first_server_p + i
+            mport = first_monitor_p + i
+
+            server_cmd = f"{cmd} --aport {port} --mport {mport} --no-render  --no-realtime"
+
             self.rcss_processes.append(
-                subprocess.Popen((f"{cmd} --aport {first_server_p+i} --mport {first_monitor_p+i}").split(),
-                stdout=subprocess.DEVNULL, stderr=subprocess.STDOUT, start_new_session=True)
+                subprocess.Popen(
+                    server_cmd.split(),
+                    stdout=subprocess.DEVNULL,
+                    stderr=subprocess.STDOUT,
+                    start_new_session=True
+                )
             )
 
     def check_running_servers(self, psutil, first_server_p, first_monitor_p, n_servers):
         ''' Check if any server is running on chosen ports '''
         found = False
         p_list = [p for p in psutil.process_iter() if p.cmdline() and "rcssservermj" in " ".join(p.cmdline())]
-        range1 = (first_server_p, first_server_p  + n_servers)
-        range2 = (first_monitor_p,first_monitor_p + n_servers)
+        range1 = (first_server_p, first_server_p + n_servers)
+        range2 = (first_monitor_p, first_monitor_p + n_servers)
         bad_processes = []
 
         for p in p_list:
             # currently ignoring remaining default port when only one of the ports is specified (uncommon scenario)
             ports = [int(arg) for arg in p.cmdline()[1:] if arg.isdigit()]
             if len(ports) == 0:
-                ports = [60000,60100] # default server ports (changing this is unlikely)
+                ports = [60000, 60100]  # default server ports (changing this is unlikely)
 
             conflicts = [str(port) for port in ports if (
-                (range1[0] <= port < range1[1]) or (range2[0] <= port < range2[1]) )]
+                    (range1[0] <= port < range1[1]) or (range2[0] <= port < range2[1]))]
 
-            if len(conflicts)>0:
+            if len(conflicts) > 0:
                 if not found:
                     print("\nThere are already servers running on the same port(s)!")
                     found = True
@@ -56,7 +65,6 @@ class Server():
                         p.kill()
                     return
 
-
     def kill(self):
         for p in self.rcss_processes:
             p.kill()

scripts/commons/Train_Base.py

@@ -31,9 +31,9 @@ class Train_Base():
         args = script.args
         self.script = script
         self.ip = args.i
-        self.server_p = args.p              # (initial) server port
-        self.monitor_p = args.m + 100           # monitor port when testing
-        self.monitor_p_1000 = args.m + 1100 # initial monitor port when training
+        self.server_p = args.p  # (initial) server port
+        self.monitor_p = args.m + 100  # monitor port when testing
+        self.monitor_p_1000 = args.m + 1100  # initial monitor port when training
         self.robot_type = args.r
         self.team = args.t
         self.uniform = args.u
@@ -41,21 +41,19 @@ class Train_Base():
         self.cf_delay = 0
         # self.cf_target_period = World.STEPTIME # target simulation speed while testing (default: real-time)
 
-    
-
     @staticmethod
     def prompt_user_for_model(self):
 
-        gyms_logs_path = "./mujococodebase/scripts/gyms/logs/"
+        gyms_logs_path = "./scripts/gyms/logs/"
         folders = [f for f in listdir(gyms_logs_path) if isdir(join(gyms_logs_path, f))]
-        folders.sort(key=lambda f: os.path.getmtime(join(gyms_logs_path, f)), reverse=True) # sort by modification date
+        folders.sort(key=lambda f: os.path.getmtime(join(gyms_logs_path, f)), reverse=True)  # sort by modification date
 
         while True:
             try:
-                folder_name = UI.print_list(folders,prompt="Choose folder (ctrl+c to return): ")[1]
+                folder_name = UI.print_list(folders, prompt="Choose folder (ctrl+c to return): ")[1]
             except KeyboardInterrupt:
                 print()
-                return None # ctrl+c
+                return None  # ctrl+c
 
             folder_dir = os.path.join(gyms_logs_path, folder_name)
             models = [m[:-4] for m in listdir(folder_dir) if isfile(join(folder_dir, m)) and m.endswith(".zip")]
@@ -64,16 +62,17 @@ class Train_Base():
                 print("The chosen folder does not contain any .zip file!")
                 continue
 
-            models.sort(key=lambda m: os.path.getmtime(join(folder_dir, m+".zip")), reverse=True) # sort by modification date
+            models.sort(key=lambda m: os.path.getmtime(join(folder_dir, m + ".zip")),
+                        reverse=True)  # sort by modification date
 
             try:
-                model_name = UI.print_list(models,prompt="Choose model (ctrl+c to return): ")[1]
+                model_name = UI.print_list(models, prompt="Choose model (ctrl+c to return): ")[1]
                 break
             except KeyboardInterrupt:
                 print()
 
-        return {"folder_dir":folder_dir, "folder_name":folder_name, "model_file":os.path.join(folder_dir, model_name+".zip")}
-
+        return {"folder_dir": folder_dir, "folder_name": folder_name,
+                "model_file": os.path.join(folder_dir, model_name + ".zip")}
 
     # def control_fps(self, read_input = False):
     #     ''' Add delay to control simulation speed '''
@@ -108,8 +107,8 @@ class Train_Base():
     #     else:
     #         self.cf_delay = 0
 
-
-    def test_model(self, model:BaseAlgorithm, env, log_path:str=None, model_path:str=None, max_episodes=0, enable_FPS_control=True, verbose=1):
+    def test_model(self, model: BaseAlgorithm, env, log_path: str = None, model_path: str = None, max_episodes=0,
+                   enable_FPS_control=True, verbose=1):
         '''
         Test model and log results
 
@@ -152,7 +151,7 @@ class Train_Base():
                 f.write("reward,ep. length,rew. cumulative avg., ep. len. cumulative avg.\n")
             print("Train statistics are saved to:", log_path)
 
-        if enable_FPS_control: # control simulation speed (using non blocking user input)
+        if enable_FPS_control:  # control simulation speed (using non blocking user input)
             print("\nThe simulation speed can be changed by sending a non-negative integer\n"
                   "(e.g. '50' sets speed to 50%, '0' pauses the simulation, '' sets speed to MAX)\n")
 
@@ -172,7 +171,7 @@ class Train_Base():
             ep_reward += reward
             ep_length += 1
 
-            if enable_FPS_control: # control simulation speed (using non blocking user input)
+            if enable_FPS_control:  # control simulation speed (using non blocking user input)
                 self.control_fps(select.select([sys.stdin], [], [], 0)[0])
 
             if done:
@@ -182,12 +181,14 @@ class Train_Base():
                 reward_max = max(ep_reward, reward_max)
                 reward_min = min(ep_reward, reward_min)
                 ep_no += 1
-                avg_ep_lengths = ep_lengths_sum/ep_no
-                avg_rewards = rewards_sum/ep_no
+                avg_ep_lengths = ep_lengths_sum / ep_no
+                avg_rewards = rewards_sum / ep_no
 
                 if verbose > 0:
-                    print(  f"\rEpisode: {ep_no:<3}  Ep.Length: {ep_length:<4.0f}  Reward: {ep_reward:<6.2f}                                                             \n",
-                        end=f"--AVERAGE--   Ep.Length: {avg_ep_lengths:<4.0f}  Reward: {avg_rewards:<6.2f}  (Min: {reward_min:<6.2f}  Max: {reward_max:<6.2f})", flush=True)
+                    print(
+                        f"\rEpisode: {ep_no:<3}  Ep.Length: {ep_length:<4.0f}  Reward: {ep_reward:<6.2f}                                                             \n",
+                        end=f"--AVERAGE--   Ep.Length: {avg_ep_lengths:<4.0f}  Reward: {avg_rewards:<6.2f}  (Min: {reward_min:<6.2f}  Max: {reward_max:<6.2f})",
+                        flush=True)
 
                 if log_path is not None:
                     with open(log_path, 'a') as f:
@@ -200,7 +201,8 @@ class Train_Base():
                 ep_reward = 0
                 ep_length = 0
 
-    def learn_model(self, model:BaseAlgorithm, total_steps:int, path:str, eval_env=None, eval_freq=None, eval_eps=5, save_freq=None, backup_env_file=None, export_name=None):
+    def learn_model(self, model: BaseAlgorithm, total_steps: int, path: str, eval_env=None, eval_freq=None, eval_eps=5,
+                    save_freq=None, backup_env_file=None, export_name=None):
         '''
         Learn Model for a specific number of time steps
 
@@ -251,7 +253,7 @@ class Train_Base():
         # If path already exists, add suffix to avoid overwriting
         if os.path.isdir(path):
             for i in count():
-                p = path.rstrip("/")+f'_{i:03}/'
+                p = path.rstrip("/") + f'_{i:03}/'
                 if not os.path.isdir(p):
                     path = p
                     break
@@ -265,22 +267,28 @@ class Train_Base():
         evaluate = bool(eval_env is not None and eval_freq is not None)
 
         # Create evaluation callback
-        eval_callback = None if not evaluate else EvalCallback(eval_env, n_eval_episodes=eval_eps, eval_freq=eval_freq, log_path=path,
-                                                               best_model_save_path=path, deterministic=True, render=False)
+        eval_callback = None if not evaluate else EvalCallback(eval_env, n_eval_episodes=eval_eps, eval_freq=eval_freq,
+                                                               log_path=path,
+                                                               best_model_save_path=path, deterministic=True,
+                                                               render=False)
 
         # Create custom callback to display evaluations
-        custom_callback = None if not evaluate else Cyclic_Callback(eval_freq, lambda:self.display_evaluations(path,True))
+        custom_callback = None if not evaluate else Cyclic_Callback(eval_freq,
+                                                                    lambda: self.display_evaluations(path, True))
 
         # Create checkpoint callback
-        checkpoint_callback = None if save_freq is None else CheckpointCallback(save_freq=save_freq, save_path=path, name_prefix="model", verbose=1)
+        checkpoint_callback = None if save_freq is None else CheckpointCallback(save_freq=save_freq, save_path=path,
+                                                                                name_prefix="model", verbose=1)
 
         # Create custom callback to export checkpoint models
-        export_callback = None if save_freq is None or export_name is None else Export_Callback(save_freq, path, export_name)
+        export_callback = None if save_freq is None or export_name is None else Export_Callback(save_freq, path,
+                                                                                                export_name)
 
-        callbacks = CallbackList([c for c in [eval_callback, custom_callback, checkpoint_callback, export_callback] if c is not None])
+        callbacks = CallbackList(
+            [c for c in [eval_callback, custom_callback, checkpoint_callback, export_callback] if c is not None])
 
-        model.learn( total_timesteps=total_steps, callback=callbacks )
-        model.save( os.path.join(path, "last_model") )
+        model.learn(total_timesteps=total_steps, callback=callbacks)
+        model.save(os.path.join(path, "last_model"))
 
         # Display evaluations if they exist
         if evaluate:
@@ -288,7 +296,7 @@ class Train_Base():
 
         # Display timestamps + Model path
         end_date = datetime.now().strftime('%d/%m/%Y %H:%M:%S')
-        duration = timedelta(seconds=int(time.time()-start))
+        duration = timedelta(seconds=int(time.time() - start))
         print(f"Train start:     {start_date}")
         print(f"Train end:       {end_date}")
         print(f"Train duration:  {duration}")
@@ -298,8 +306,8 @@ class Train_Base():
         if backup_env_file is not None:
             with open(backup_file, 'a') as f:
                 f.write(f"\n# Train start:    {start_date}\n")
-                f.write(  f"# Train end:      {end_date}\n")
-                f.write(  f"# Train duration: {duration}")
+                f.write(f"# Train end:      {end_date}\n")
+                f.write(f"# Train duration: {duration}")
 
         return path
 
@@ -318,50 +326,57 @@ class Train_Base():
 
         with np.load(eval_npz) as data:
             time_steps = data["timesteps"]
-            results_raw = np.mean(data["results"],axis=1)
-            ep_lengths_raw = np.mean(data["ep_lengths"],axis=1)
+            results_raw = np.mean(data["results"], axis=1)
+            ep_lengths_raw = np.mean(data["ep_lengths"], axis=1)
         sample_no = len(results_raw)
 
-        xvals = np.linspace(0, sample_no-1, 80)
-        results    = np.interp(xvals, range(sample_no), results_raw)
+        xvals = np.linspace(0, sample_no - 1, 80)
+        results = np.interp(xvals, range(sample_no), results_raw)
         ep_lengths = np.interp(xvals, range(sample_no), ep_lengths_raw)
 
-        results_limits    = np.min(results),    np.max(results)
+        results_limits = np.min(results), np.max(results)
         ep_lengths_limits = np.min(ep_lengths), np.max(ep_lengths)
 
-        results_discrete    = np.digitize(results,    np.linspace(results_limits[0]-1e-5, results_limits[1]+1e-5,    console_height+1))-1
-        ep_lengths_discrete = np.digitize(ep_lengths, np.linspace(0,                      ep_lengths_limits[1]+1e-5, console_height+1))-1
+        results_discrete = np.digitize(results, np.linspace(results_limits[0] - 1e-5, results_limits[1] + 1e-5,
+                                                            console_height + 1)) - 1
+        ep_lengths_discrete = np.digitize(ep_lengths,
+                                          np.linspace(0, ep_lengths_limits[1] + 1e-5, console_height + 1)) - 1
 
         matrix = np.zeros((console_height, console_width, 2), int)
-        matrix[results_discrete[0]   ][0][0] = 1    # draw 1st column
-        matrix[ep_lengths_discrete[0]][0][1] = 1    # draw 1st column
+        matrix[results_discrete[0]][0][0] = 1  # draw 1st column
+        matrix[ep_lengths_discrete[0]][0][1] = 1  # draw 1st column
         rng = [[results_discrete[0], results_discrete[0]], [ep_lengths_discrete[0], ep_lengths_discrete[0]]]
 
         # Create continuous line for both plots
         for k in range(2):
-            for i in range(1,console_width):
+            for i in range(1, console_width):
                 x = [results_discrete, ep_lengths_discrete][k][i]
                 if x > rng[k][1]:
-                    rng[k] = [rng[k][1]+1, x]
+                    rng[k] = [rng[k][1] + 1, x]
                 elif x < rng[k][0]:
-                    rng[k] = [x, rng[k][0]-1]
+                    rng[k] = [x, rng[k][0] - 1]
                 else:
-                    rng[k] = [x,x]
-                for j in range(rng[k][0],rng[k][1]+1):
+                    rng[k] = [x, x]
+                for j in range(rng[k][0], rng[k][1] + 1):
                     matrix[j][i][k] = 1
 
-        print(f'{"-"*console_width}')
+        print(f'{"-" * console_width}')
         for l in reversed(range(console_height)):
             for c in range(console_width):
-                if   np.all(matrix[l][c] == 0): print(end=" ")
-                elif np.all(matrix[l][c] == 1): print(end=symb_xo)
-                elif matrix[l][c][0] == 1:      print(end=symb_x)
-                else:                           print(end=symb_o)
+                if np.all(matrix[l][c] == 0):
+                    print(end=" ")
+                elif np.all(matrix[l][c] == 1):
+                    print(end=symb_xo)
+                elif matrix[l][c][0] == 1:
+                    print(end=symb_x)
+                else:
+                    print(end=symb_o)
             print()
-        print(f'{"-"*console_width}')
+        print(f'{"-" * console_width}')
         print(f"({symb_x})-reward          min:{results_limits[0]:11.2f}    max:{results_limits[1]:11.2f}")
-        print(f"({symb_o})-ep. length      min:{ep_lengths_limits[0]:11.0f}    max:{ep_lengths_limits[1]:11.0f}    {time_steps[-1]/1000:15.0f}k steps")
-        print(f'{"-"*console_width}')
+        print(
+            f"({symb_o})-ep. length      min:{ep_lengths_limits[0]:11.0f}    max:{ep_lengths_limits[1]:11.0f}    {time_steps[-1] / 1000:15.0f}k steps")
+        print(f'{"-" * console_width}')
 
         # save CSV
         if save_csv:
@@ -370,8 +385,7 @@ class Train_Base():
                 writer = csv.writer(f)
                 if sample_no == 1:
                     writer.writerow(["time_steps", "reward ep.", "length"])
-                writer.writerow([time_steps[-1],results_raw[-1],ep_lengths_raw[-1]])
-
+                writer.writerow([time_steps[-1], results_raw[-1], ep_lengths_raw[-1]])
 
     # def generate_slot_behavior(self, path, slots, auto_head:bool, XML_name):
     #     '''
@@ -454,21 +468,21 @@ class Train_Base():
                     break
 
         model = PPO.load(input_file)
-        weights = model.policy.state_dict() # dictionary containing network layers
+        weights = model.policy.state_dict()  # dictionary containing network layers
 
-        w = lambda name : weights[name].detach().cpu().numpy() # extract weights from policy
+        w = lambda name: weights[name].detach().cpu().numpy()  # extract weights from policy
 
         var_list = []
-        for i in count(0,2): # add hidden layers (step=2 because that's how SB3 works)
+        for i in count(0, 2):  # add hidden layers (step=2 because that's how SB3 works)
             if f"mlp_extractor.policy_net.{i}.bias" not in weights:
                 break
-            var_list.append([w(f"mlp_extractor.policy_net.{i}.bias"), w(f"mlp_extractor.policy_net.{i}.weight"), "tanh"])
+            var_list.append(
+                [w(f"mlp_extractor.policy_net.{i}.bias"), w(f"mlp_extractor.policy_net.{i}.weight"), "tanh"])
 
-        var_list.append( [w("action_net.bias"), w("action_net.weight"), "none"] ) # add final layer
-        
-        with open(output_file,"wb") as f:
-            pickle.dump(var_list, f, protocol=4) # protocol 4 is backward compatible with Python 3.4
+        var_list.append([w("action_net.bias"), w("action_net.weight"), "none"])  # add final layer
 
+        with open(output_file, "wb") as f:
+            pickle.dump(var_list, f, protocol=4)  # protocol 4 is backward compatible with Python 3.4
 
     def print_list(data, numbering=True, prompt=None, divider=" | ", alignment="<", min_per_col=6):
         '''
@@ -503,58 +517,60 @@ class Train_Base():
         items = []
         items_len = []
 
-        #--------------------------------------------- Add numbers, margins and divider
+        # --------------------------------------------- Add numbers, margins and divider
         for i in range(data_size):
             number = f"{i}-" if numbering else ""
-            items.append( f"{divider}{number}{data[i]}" )
-            items_len.append( len(items[-1]) )
+            items.append(f"{divider}{number}{data[i]}")
+            items_len.append(len(items[-1]))
 
-        max_cols = np.clip((WIDTH+len(divider)) // min(items_len),1,math.ceil(data_size/max(min_per_col,1))) # width + len(divider) because it is not needed in last col
+        max_cols = np.clip((WIDTH + len(divider)) // min(items_len), 1, math.ceil(
+            data_size / max(min_per_col, 1)))  # width + len(divider) because it is not needed in last col
 
-        #--------------------------------------------- Check maximum number of columns, considering content width (min:1)
-        for i in range(max_cols,0,-1):
+        # --------------------------------------------- Check maximum number of columns, considering content width (min:1)
+        for i in range(max_cols, 0, -1):
             cols_width = []
             cols_items = []
             table_width = 0
-            a,b = divmod(data_size,i)
+            a, b = divmod(data_size, i)
             for col in range(i):
-                start = a*col + min(b,col)
-                end = start+a+(1 if col<b else 0)
-                cols_items.append( items[start:end] )
+                start = a * col + min(b, col)
+                end = start + a + (1 if col < b else 0)
+                cols_items.append(items[start:end])
                 col_width = max(items_len[start:end])
-                cols_width.append( col_width )
+                cols_width.append(col_width)
                 table_width += col_width
-            if table_width <= WIDTH+len(divider):
+            if table_width <= WIDTH + len(divider):
                 break
         table_width -= len(divider)
 
-        #--------------------------------------------- Print columns
-        print("="*table_width)
+        # --------------------------------------------- Print columns
+        print("=" * table_width)
         for row in range(math.ceil(data_size / i)):
             for col in range(i):
-                content = cols_items[col][row] if len(cols_items[col]) > row else divider # print divider when there are no items
+                content = cols_items[col][row] if len(
+                    cols_items[col]) > row else divider  # print divider when there are no items
                 if col == 0:
                     l = len(divider)
-                    print(end=f"{content[l:]:{alignment}{cols_width[col]-l}}")  # remove divider from 1st col
+                    print(end=f"{content[l:]:{alignment}{cols_width[col] - l}}")  # remove divider from 1st col
                 else:
-                    print(end=f"{content    :{alignment}{cols_width[col]  }}") 
+                    print(end=f"{content    :{alignment}{cols_width[col]}}")
             print()
-        print("="*table_width)
+        print("=" * table_width)
 
-        #--------------------------------------------- Prompt
+        # --------------------------------------------- Prompt
         if prompt is None:
             return None
 
         if numbering is None:
             return None
         else:
-            idx = UI.read_int( prompt, 0, data_size )
+            idx = UI.read_int(prompt, 0, data_size)
             return idx, data[idx]
 
 
-
 class Cyclic_Callback(BaseCallback):
     ''' Stable baselines custom callback '''
+
     def __init__(self, freq, function):
         super(Cyclic_Callback, self).__init__(1)
         self.freq = freq
@@ -563,10 +579,12 @@ class Cyclic_Callback(BaseCallback):
     def _on_step(self) -> bool:
         if self.n_calls % self.freq == 0:
             self.function()
-        return True # If the callback returns False, training is aborted early
+        return True  # If the callback returns False, training is aborted early
+
 
 class Export_Callback(BaseCallback):
     ''' Stable baselines custom callback '''
+
     def __init__(self, freq, load_path, export_name):
         super(Export_Callback, self).__init__(1)
         self.freq = freq
@@ -577,8 +595,7 @@ class Export_Callback(BaseCallback):
         if self.n_calls % self.freq == 0:
             path = os.path.join(self.load_path, f"model_{self.num_timesteps}_steps.zip")
             Train_Base.export_model(path, f"./scripts/gyms/export/{self.export_name}")
-        return True # If the callback returns False, training is aborted early
-    
+        return True  # If the callback returns False, training is aborted early
 
 
 

scripts/gyms/Walk.py

@@ -1,11 +1,11 @@
 import os
 import numpy as np
 import math
+import time
 from time import sleep
 from random import random
 from random import uniform
 
-
 from stable_baselines3 import PPO
 from stable_baselines3.common.vec_env import SubprocVecEnv
 
@@ -28,25 +28,24 @@ Learn how to run forward using step primitive
 - class Train:  implements algorithms to train a new model or test an existing model
 '''
 
+
 class WalkEnv(gym.Env):
     def __init__(self, ip, server_p) -> None:
 
-        
-
         # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
         self.Player = player = Base_Agent(
-                team_name="Gym",
-                number=1,
-                host=ip,
-                port=server_p
+            team_name="Gym",
+            number=1,
+            host=ip,
+            port=server_p
         )
         self.robot_type = self.Player.robot
-        self.step_counter = 0 # to limit episode size
+        self.step_counter = 0  # to limit episode size
         self.force_play_on = True
 
-        self.target_position = np.array([0.0, 0.0]) # target position in the x-y plane 
-        self.initial_position = np.array([0.0, 0.0]) # initial position in the x-y plane
-        self.target_direction = 0.0 # target direction in the x-y plane (relative to the robot's orientation)
+        self.target_position = np.array([0.0, 0.0])  # target position in the x-y plane
+        self.initial_position = np.array([0.0, 0.0])  # initial position in the x-y plane
+        self.target_direction = 0.0  # target direction in the x-y plane (relative to the robot's orientation)
         self.isfallen = False
         self.waypoint_index = 0
         self.route_completed = False
@@ -55,7 +54,17 @@ class WalkEnv(gym.Env):
         self.auto_calibrate_train_sim_flip = True
         self.nominal_calibrated_once = False
         self.flip_calibrated_once = False
-
+        self._target_hz = 0.0
+        self._target_dt = 0.0
+        self._last_sync_time = None
+        target_hz_env = 24
+        if target_hz_env:
+            try:
+                self._target_hz = float(target_hz_env)
+            except ValueError:
+                self._target_hz = 0.0
+        if self._target_hz > 0.0:
+            self._target_dt = 1.0 / self._target_hz
 
         # State space
         # 原始观测大小: 78
@@ -77,7 +86,6 @@ class WalkEnv(gym.Env):
             dtype=np.float32
         )
 
-        
         # 中立姿态
         self.joint_nominal_position = np.array(
             [
@@ -110,22 +118,22 @@ class WalkEnv(gym.Env):
 
         self.train_sim_flip = np.array(
             [
-                1.0,   # 0: Head_yaw (he1)
+                1.0,  # 0: Head_yaw (he1)
                 -1.0,  # 1: Head_pitch (he2)
-                1.0,   # 2: Left_Shoulder_Pitch (lae1)
+                1.0,  # 2: Left_Shoulder_Pitch (lae1)
                 -1.0,  # 3: Left_Shoulder_Roll (lae2)
-                1.0,   # 4: Left_Elbow_Pitch (lae3)
-                1.0,   # 5: Left_Elbow_Yaw (lae4)
+                1.0,  # 4: Left_Elbow_Pitch (lae3)
+                1.0,  # 5: Left_Elbow_Yaw (lae4)
                 -1.0,  # 6: Right_Shoulder_Pitch (rae1)
-                1.0,   # 7: Right_Shoulder_Roll (rae2)
-                1.0,   # 8: Right_Elbow_Pitch (rae3)
-                1.0,   # 9: Right_Elbow_Yaw (rae4)
-                1.0,   # 10: Waist (te1)
-                1.0,   # 11: Left_Hip_Pitch (lle1)
+                1.0,  # 7: Right_Shoulder_Roll (rae2)
+                1.0,  # 8: Right_Elbow_Pitch (rae3)
+                1.0,  # 9: Right_Elbow_Yaw (rae4)
+                1.0,  # 10: Waist (te1)
+                1.0,  # 11: Left_Hip_Pitch (lle1)
                 -1.0,  # 12: Left_Hip_Roll (lle2)
                 -1.0,  # 13: Left_Hip_Yaw (lle3)
-                1.0,   # 14: Left_Knee_Pitch (lle4)
-                1.0,   # 15: Left_Ankle_Pitch (lle5)
+                1.0,  # 14: Left_Knee_Pitch (lle4)
+                1.0,  # 15: Left_Ankle_Pitch (lle5)
                 -1.0,  # 16: Left_Ankle_Roll (lle6)
                 -1.0,  # 17: Right_Hip_Pitch (rle1)
                 -1.0,  # 18: Right_Hip_Roll (rle2)
@@ -141,7 +149,7 @@ class WalkEnv(gym.Env):
         self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
         self.previous_pos = np.array([0.0, 0.0])  # Track previous position
         self.Player.server.connect()
-        sleep(2.0)  # Longer wait for connection to establish completely
+        # sleep(2.0)  # Longer wait for connection to establish completely
         self.Player.server.send_immediate(
             f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
         )
@@ -160,7 +168,7 @@ class WalkEnv(gym.Env):
         changed = []
 
         for idx, (reference_value, observed_value) in enumerate(
-            zip(self.reference_joint_nominal_position, neutral_joint_positions)
+                zip(self.reference_joint_nominal_position, neutral_joint_positions)
         ):
             if idx >= 10:
                 continue
@@ -187,14 +195,13 @@ class WalkEnv(gym.Env):
         height = float(self.Player.world.global_position[2])
 
         reliable = (
-            leg_norm > 0.8
-            and leg_max > 0.35
-            and 0.12 < height < 0.8
+                leg_norm > 0.8
+                and leg_max > 0.35
+                and 0.12 < height < 0.8
         )
 
         return reliable, leg_norm, leg_max, height
 
-
     def observe(self, init=False):
 
         """获取当前观测值"""
@@ -240,7 +247,6 @@ class WalkEnv(gym.Env):
         orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
         projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
 
-        
         # 组合观测
         observation = np.concatenate([
             qpos_qvel_previous_action,
@@ -249,8 +255,6 @@ class WalkEnv(gym.Env):
             projected_gravity,
         ])
 
-        
-        
         observation = np.clip(observation, -10.0, 10.0)
         return observation.astype(np.float32)
 
@@ -260,6 +264,17 @@ class WalkEnv(gym.Env):
         self.Player.world.update()
         self.Player.robot.commit_motor_targets_pd()
         self.Player.server.send()
+        if self._target_dt > 0.0:
+            now = time.time()
+            if self._last_sync_time is None:
+                self._last_sync_time = now
+                return
+            elapsed = now - self._last_sync_time
+            remaining = self._target_dt - elapsed
+            if remaining > 0.0:
+                time.sleep(remaining)
+                now = time.time()
+            self._last_sync_time = now
 
     def debug_joint_status(self):
         robot = self.Player.robot
@@ -295,12 +310,11 @@ class WalkEnv(gym.Env):
         if seed is not None:
             np.random.seed(seed)
 
-        length1 = np.random.uniform(10, 20) # randomize target distance
-        length2 = np.random.uniform(10, 20) # randomize target distance
-        length3 = np.random.uniform(10, 20) # randomize target distance
-        angle2 = np.random.uniform(-30, 30) # randomize initial orientation
-        angle3 = np.random.uniform(-30, 30) # randomize target direction
-
+        length1 = np.random.uniform(10, 20)  # randomize target distance
+        length2 = np.random.uniform(10, 20)  # randomize target distance
+        length3 = np.random.uniform(10, 20)  # randomize target distance
+        angle2 = np.random.uniform(-30, 30)  # randomize initial orientation
+        angle3 = np.random.uniform(-30, 30)  # randomize target direction
 
         self.step_counter = 0
         self.waypoint_index = 0
@@ -322,12 +336,12 @@ class WalkEnv(gym.Env):
 
         # 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
         finished_count = 0
-        for _ in range(20):
+        for _ in range(10):
             finished = self.Player.skills_manager.execute("Neutral")
             self.sync()
             if finished:
                 finished_count += 1
-                if finished_count >= 2:  # 假设需要连续2次完成才算成功
+                if finished_count >= 3:  # 假设需要连续3次完成才算成功
                     break
 
         # neutral_joint_positions = np.deg2rad(
@@ -356,17 +370,15 @@ class WalkEnv(gym.Env):
         # reset_action_noise = np.random.uniform(-0.015, 0.015, size=(len(self.Player.robot.ROBOT_MOTORS),))
         # self.target_joint_positions = (self.joint_nominal_position + reset_action_noise) * self.train_sim_flip
 
-
         # for idx, target in enumerate(self.target_joint_positions):
         #     r.set_motor_target_position(
         #         r.ROBOT_MOTORS[idx], target*180/math.pi, kp=25, kd=0.6
         #     )
 
-
         # memory variables
         self.initial_position = np.array(self.Player.world.global_position[:2])
         self.previous_pos = self.initial_position.copy()  # Critical: set to actual position
-        self.act = np.zeros(self.no_of_actions ,np.float32)
+        self.act = np.zeros(self.no_of_actions, np.float32)
         point1 = self.initial_position + np.array([length1, 0])
         point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False)
         point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False)
@@ -423,40 +435,35 @@ class WalkEnv(gym.Env):
         tilt_penalty = -0.2 * np.linalg.norm(self.Player.robot.gyroscope[:2]) / 100.0
 
         return (
-            progress_reward
-            + speed_reward
-            + direction_reward
-            + alive_bonus
-            + height_reward
-            + motionless_penalty
-            + waypoint_bonus
-            + action_penalty
-            + tilt_penalty
+                progress_reward
+                + speed_reward
+                + direction_reward
+                + alive_bonus
+                + height_reward
+                + motionless_penalty
+                + waypoint_bonus
+                + action_penalty
+                + tilt_penalty
         )
 
     def step(self, action):
 
         r = self.Player.robot
 
-
         self.previous_action = action
 
         self.target_joint_positions = (
-            self.joint_nominal_position
-            + self.scaling_factor * action
+                self.joint_nominal_position
+                + self.scaling_factor * action
         )
         self.target_joint_positions *= self.train_sim_flip
 
-
         for idx, target in enumerate(self.target_joint_positions):
             r.set_motor_target_position(
-                r.ROBOT_MOTORS[idx], target*180/math.pi, kp=25, kd=0.6
+                r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
             )
 
-        
-
-        
-        self.sync() # run simulation step
+        self.sync()  # run simulation step
         self.step_counter += 1
 
         # if self.step_counter % self.debug_every_n_steps == 0:
@@ -467,7 +474,6 @@ class WalkEnv(gym.Env):
         # Compute reward based on movement from previous step
         reward = self.compute_reward(self.previous_pos, current_pos, action)
 
-        
         # Update previous position
         self.previous_pos = current_pos.copy()
 
@@ -481,20 +487,18 @@ class WalkEnv(gym.Env):
         return self.observe(), reward, terminated, truncated, {}
 
 
-
-
-
 class Train(Train_Base):
     def __init__(self, script) -> None:
         super().__init__(script)
 
-
     def train(self, args):
 
-        #--------------------------------------- Learning parameters
+        # --------------------------------------- Learning parameters
         n_envs = 8  # Reduced from 8 to decrease CPU/network pressure during init
+        if n_envs < 1:
+            raise ValueError("GYM_CPU_N_ENVS must be >= 1")
         n_steps_per_env = 512  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = 64    # should be a factor of (n_steps_per_env * n_envs)
+        minibatch_size = 128  # should be a factor of (n_steps_per_env * n_envs)
         total_steps = 30000000
         learning_rate = 3e-4
         folder_name = f'Walk_R{self.robot_type}'
@@ -503,35 +507,38 @@ class Train(Train_Base):
         print(f"Model path: {model_path}")
         print(f"Using {n_envs} parallel environments")
 
-        #--------------------------------------- Run algorithm
+        # --------------------------------------- Run algorithm
         def init_env(i_env):
             def thunk():
-                return WalkEnv( self.ip , self.server_p + i_env)
+                return WalkEnv(self.ip, self.server_p + i_env)
+
             return thunk
 
-        servers = Train_Server( self.server_p, self.monitor_p_1000, n_envs+1 ) #include 1 extra server for testing
+        server_log_dir = os.path.join(model_path, "server_logs")
+        os.makedirs(server_log_dir, exist_ok=True)
+        servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1)  # include 1 extra server for testing
 
         # Wait for servers to start
-        print(f"Starting {n_envs+1} rcssservermj servers...")
+        print(f"Starting {n_envs + 1} rcssservermj servers...")
         print("Servers started, creating environments...")
 
-        env = SubprocVecEnv( [init_env(i) for i in range(n_envs)] )
-        eval_env = SubprocVecEnv( [init_env(n_envs)] )
-        
+        env = SubprocVecEnv([init_env(i) for i in range(n_envs)])
+        eval_env = SubprocVecEnv([init_env(n_envs)])
 
         try:
             # Custom policy network architecture
             policy_kwargs = dict(
                 net_arch=dict(
                     pi=[256, 256, 128],  # Policy network: 3 layers
-                    vf=[256, 256, 128]   # Value network: 3 layers  
+                    vf=[256, 256, 128]  # Value network: 3 layers
                 ),
                 activation_fn=__import__('torch.nn', fromlist=['ReLU']).ReLU,
             )
 
-            if "model_file" in args: # retrain
-                model = PPO.load( args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env, batch_size=minibatch_size, learning_rate=learning_rate )
-            else: # train new model
+            if "model_file" in args:  # retrain
+                model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
+                                 batch_size=minibatch_size, learning_rate=learning_rate)
+            else:  # train new model
                 model = PPO(
                     "MlpPolicy",
                     env=env,
@@ -547,9 +554,11 @@ class Train(Train_Base):
                     gamma=0.99  # Discount factor
                 )
 
-            model_path = self.learn_model( model, total_steps, model_path, eval_env=eval_env, eval_freq=n_steps_per_env*20, save_freq=n_steps_per_env*20, backup_env_file=__file__ )
+            model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
+                                          eval_freq=n_steps_per_env * 10, save_freq=n_steps_per_env * 10,
+                                          backup_env_file=__file__)
         except KeyboardInterrupt:
-            sleep(1) # wait for child processes
+            sleep(1)  # wait for child processes
             print("\nctrl+c pressed, aborting...\n")
             servers.kill()
             return
@@ -558,17 +567,19 @@ class Train(Train_Base):
         eval_env.close()
         servers.kill()
 
-
     def test(self, args):
 
         # Uses different server and monitor ports
-        server = Train_Server( self.server_p-1, self.monitor_p, 1 )
-        env = WalkEnv( self.ip, self.server_p-1 )
-        model = PPO.load( args["model_file"], env=env )
+        server_log_dir = os.path.join(args["folder_dir"], "server_logs")
+        os.makedirs(server_log_dir, exist_ok=True)
+        server = Train_Server(self.server_p - 1, self.monitor_p, 1, log_dir=server_log_dir)
+        env = WalkEnv(self.ip, self.server_p - 1)
+        model = PPO.load(args["model_file"], env=env)
 
         try:
-            self.export_model( args["model_file"], args["model_file"]+".pkl", False )  # Export to pkl to create custom behavior
-            self.test_model( model, env, log_path=args["folder_dir"], model_path=args["folder_dir"] )
+            self.export_model(args["model_file"], args["model_file"] + ".pkl",
+                              False)  # Export to pkl to create custom behavior
+            self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
         except KeyboardInterrupt:
             print()
 
@@ -583,13 +594,13 @@ if __name__ == "__main__":
     script_args = SimpleNamespace(
         args=SimpleNamespace(
             i='127.0.0.1',  # Server IP
-            p=3100,         # Server port
-            m=3200,         # Monitor port
-            r=0,            # Robot type
-            t='Gym',        # Team name
-            u=1             # Uniform number
+            p=3100,  # Server port
+            m=3200,  # Monitor port
+            r=0,  # Robot type
+            t='Gym',  # Team name
+            u=1  # Uniform number
         )
     )
 
     trainer = Train(script_args)
-    trainer.train({})
+    trainer.train({"model_file": "scripts/gyms/logs/Walk_R0_000/model_245760_steps.zip"})