stand stable 0.1 amendment and add some info

.gitignore

@@ -10,3 +10,13 @@ poetry.toml
 **/log/
 *.spec
 dist/
+*steps.zip
+*.pkl
+best_model.zip
+*.csv
+*.npz
+*.xml
+*.json
+*.yaml
+*.iml
+*.TXT

scripts/commons/Server.py

@@ -21,7 +21,7 @@ class Server():
             port = first_server_p + i
             mport = first_monitor_p + i
 
-            server_cmd = f"{cmd} --aport {port} --mport {mport} "
+            server_cmd = f"{cmd} -c {port} -m {mport} "
 
             self.rcss_processes.append(
                 subprocess.Popen(

scripts/gyms/Walk.py

@@ -50,6 +50,7 @@ class WalkEnv(gym.Env):
         self.waypoint_index = 0
         self.route_completed = False
         self.debug_every_n_steps = 5
+        self.enable_debug_joint_status = False
         self.calibrate_nominal_from_neutral = True
         self.auto_calibrate_train_sim_flip = True
         self.nominal_calibrated_once = False
@@ -57,7 +58,7 @@ class WalkEnv(gym.Env):
         self._target_hz = 0.0
         self._target_dt = 0.0
         self._last_sync_time = None
-        target_hz_env = 1000
+        target_hz_env = 0
         if target_hz_env:
             try:
                 self._target_hz = float(target_hz_env)
@@ -114,7 +115,6 @@ class WalkEnv(gym.Env):
                 0.0,
             ]
         )
-        self.reference_joint_nominal_position = self.joint_nominal_position.copy()
         self.joint_nominal_position = np.zeros(self.no_of_actions)
         self.train_sim_flip = np.array(
             [
@@ -144,9 +144,16 @@ class WalkEnv(gym.Env):
             ]
         )
 
-        self.scaling_factor = 0.5
+        self.scaling_factor = 0.3
         # self.scaling_factor = 1
 
+        # Small reset perturbations for robustness training.
+        self.enable_reset_perturb = True
+        self.reset_beam_yaw_range_deg = 180  # randomize target direction fully to encourage learning a real walk instead of a fixed gait
+        self.reset_joint_noise_rad = 0.015
+        self.reset_perturb_steps = 3
+        self.reset_recover_steps = 8
+
         self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
         self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
         self.previous_pos = np.array([0.0, 0.0])  # Track previous position
@@ -155,6 +162,7 @@ class WalkEnv(gym.Env):
         self.Player.server.send_immediate(
             f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
         )
+        self.start_time = time.time()
 
     def debug_log(self, message):
         print(message)
@@ -165,45 +173,6 @@ class WalkEnv(gym.Env):
         except OSError:
             pass
 
-    def calibrate_train_sim_flip_from_neutral(self, neutral_joint_positions):
-        updated_flip = self.train_sim_flip.copy()
-        changed = []
-
-        for idx, (reference_value, observed_value) in enumerate(
-                zip(self.reference_joint_nominal_position, neutral_joint_positions)
-        ):
-            if idx >= 10:
-                continue
-            if abs(reference_value) < 0.15 or abs(observed_value) < 0.15:
-                continue
-
-            inferred_flip = 1.0 if np.sign(reference_value) == np.sign(observed_value) else -1.0
-            if updated_flip[idx] != inferred_flip:
-                changed.append((idx, updated_flip[idx], inferred_flip))
-                updated_flip[idx] = inferred_flip
-
-        self.train_sim_flip = updated_flip
-
-        if changed:
-            self.debug_log(
-                "[FlipDebug] "
-                f"changes={[(idx, old, new) for idx, old, new in changed]}"
-            )
-
-    def is_reliable_neutral_pose(self, neutral_joint_positions):
-        leg_positions = neutral_joint_positions[11:]
-        leg_norm = float(np.linalg.norm(leg_positions))
-        leg_max = float(np.max(np.abs(leg_positions)))
-        height = float(self.Player.world.global_position[2])
-
-        reliable = (
-                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"""
@@ -301,6 +270,7 @@ class WalkEnv(gym.Env):
             f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
             f"fallen={self.Player.world.global_position[2] < 0.3}"
         )
+        print(f"waist target={target_joint_positions[10]:.3f}, actual={actual_joint_positions[10]:.3f}")
 
     def reset(self, seed=None, options=None):
         '''
@@ -312,9 +282,9 @@ class WalkEnv(gym.Env):
         if seed is not None:
             np.random.seed(seed)
 
-        length1 = np.random.uniform(10, 20)  # randomize target distance
+        length1 = 2  # randomize target distance
-        length2 = np.random.uniform(10, 20)  # randomize target distance
+        length2 = np.random.uniform(0.6, 1)  # randomize target distance
-        length3 = np.random.uniform(10, 20)  # randomize target distance
+        length3 = np.random.uniform(0.6, 1)  # randomize target distance
         angle2 = np.random.uniform(-30, 30)  # randomize initial orientation
         angle3 = np.random.uniform(-30, 30)  # randomize target direction
 
@@ -329,64 +299,66 @@ class WalkEnv(gym.Env):
         # 随机 beam 目标位置和朝向，增加训练多样性
         beam_x = (random() - 0.5) * 10
         beam_y = (random() - 0.5) * 10
+        beam_yaw = uniform(-self.reset_beam_yaw_range_deg, self.reset_beam_yaw_range_deg)
 
         for _ in range(5):
             self.Player.server.receive()
             self.Player.world.update()
             self.Player.robot.commit_motor_targets_pd()
-            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=0)
+            self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=beam_yaw)
             self.Player.server.send()
 
         # 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(10):
+        for _ in range(50):
             finished = self.Player.skills_manager.execute("Neutral")
             self.sync()
             if finished:
                 finished_count += 1
-                if finished_count >= 3:  # 假设需要连续3次完成才算成功
+                if finished_count >= 20:  # 假设需要连续20次完成才算成功
                     break
 
-        # neutral_joint_positions = np.deg2rad(
+        if self.enable_reset_perturb and self.reset_joint_noise_rad > 0.0:
-        #     [self.Player.robot.motor_positions[motor] for motor in self.Player.robot.ROBOT_MOTORS]
+            perturb_action = np.zeros(self.no_of_actions, dtype=np.float32)
-        # )
+            # Perturb waist + lower body only (10:), keep head/arms stable.
-        # reliable_neutral, neutral_leg_norm, neutral_leg_max, neutral_height = self.is_reliable_neutral_pose(neutral_joint_positions)
+            perturb_action[10:] = np.random.uniform(
+                -self.reset_joint_noise_rad,
+                self.reset_joint_noise_rad,
+                size=(self.no_of_actions - 10,)
+            )
 
-        # if self.auto_calibrate_train_sim_flip and reliable_neutral and not self.flip_calibrated_once:
+            for _ in range(self.reset_perturb_steps):
-        #     self.calibrate_train_sim_flip_from_neutral(neutral_joint_positions)
+                target_joint_positions = (self.joint_nominal_position + perturb_action) * self.train_sim_flip
-        #     self.flip_calibrated_once = True
+                for idx, target in enumerate(target_joint_positions):
-        # if self.calibrate_nominal_from_neutral and reliable_neutral and not self.nominal_calibrated_once:
+                    r.set_motor_target_position(
-        #     self.joint_nominal_position = neutral_joint_positions * self.train_sim_flip
+                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
-        #     self.nominal_calibrated_once = True
+                    )
-        # self.debug_log(
+                self.sync()
-        #     "[ResetDebug] "
-        #     f"neutral_pos={np.round(self.Player.world.global_position, 3).tolist()} "
-        #     f"shoulders={np.round(neutral_joint_positions[2:10], 3).tolist()} "
-        #     f"legs={np.round(neutral_joint_positions[11:], 3).tolist()} "
-        #     f"flip={self.train_sim_flip.tolist()} "
-        #     f"nominal_legs={np.round(self.joint_nominal_position[11:], 3).tolist()} "
-        #     f"calibrated_once={(self.flip_calibrated_once, self.nominal_calibrated_once)} "
-        #     f"reliable_neutral={reliable_neutral} "
-        #     f"leg_norm={neutral_leg_norm:.3f} leg_max={neutral_leg_max:.3f} height={neutral_height:.3f}"
-        # )
 
-        # reset_action_noise = np.random.uniform(-0.015, 0.015, size=(len(self.Player.robot.ROBOT_MOTORS),))
+            for i in range(self.reset_recover_steps):
-        # self.target_joint_positions = (self.joint_nominal_position + reset_action_noise) * self.train_sim_flip
+                # Linearly fade perturbation to help policy start from near-neutral.
-
+                alpha = 1.0 - float(i + 1) / float(self.reset_recover_steps)
-        # for idx, target in enumerate(self.target_joint_positions):
+                target_joint_positions = (self.joint_nominal_position + alpha * perturb_action) * self.train_sim_flip
-        #     r.set_motor_target_position(
+                for idx, target in enumerate(target_joint_positions):
-        #         r.ROBOT_MOTORS[idx], target*180/math.pi, kp=25, kd=0.6
+                    r.set_motor_target_position(
-        #     )
+                        r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
+                    )
+                self.sync()
 
         # memory variables
+        self.sync()  
         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)
-        point1 = self.initial_position + np.array([length1, 0])
+        # Build target in the robot's current forward direction instead of fixed global +x.
+        heading_deg = float(r.global_orientation_euler[2])
+        forward_offset = MathOps.rotate_2d_vec(np.array([length1, 0.0]), heading_deg, is_rad=False)
+        point1 = self.initial_position + forward_offset
         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)
-        self.point_list = [point1, point2, point3]
+        self.point_list = [point1]
         self.target_position = self.point_list[self.waypoint_index]
+        self.initial_height = self.Player.world.global_position[2]
 
         return self.observe(True), {}
 
@@ -394,89 +366,99 @@ class WalkEnv(gym.Env):
         return
 
     def compute_reward(self, previous_pos, current_pos, action):
-        eps = 1e-6
-        dt = 0.05
-
-        velocity = current_pos - previous_pos
-        speed_step = float(np.linalg.norm(velocity))
-        speed = speed_step / dt
-
-        direction_to_target = self.target_position - current_pos
-        prev_direction_to_target = self.target_position - previous_pos
-        distance_to_target = float(np.linalg.norm(direction_to_target))
-        prev_distance_to_target = float(np.linalg.norm(prev_direction_to_target))
-
-        # Progress toward waypoint (secondary signal)
-        progress = prev_distance_to_target - distance_to_target
-        progress_reward = np.clip(progress * 2.0, -1.5, 2.5)
-
-        # Forward speed and lateral drift
-        forward_dir = direction_to_target / max(distance_to_target, eps)
-        forward_speed = float(np.dot(velocity, forward_dir)) / dt
-        target_speed = 1.0
-        speed_error = forward_speed - target_speed
-        speed_reward = 3.0 * math.exp(-1.5 * (speed_error ** 2))
-
-        lateral_vec = velocity - forward_dir * np.dot(velocity, forward_dir)
-        lateral_speed = float(np.linalg.norm(lateral_vec)) / dt
-        lateral_penalty = -0.6 * np.clip(lateral_speed, 0.0, 2.0)
-
-        # Heading alignment (small shaping term)
-        if speed_step > 1e-4 and distance_to_target > 1e-4:
-            directional_alignment = np.dot(velocity, direction_to_target) / (speed_step * distance_to_target)
-            directional_alignment = float(np.clip(directional_alignment, -1.0, 1.0))
-            direction_reward = max(0.0, directional_alignment) * 0.3
-        else:
-            direction_reward = 0.0
-
-        alive_bonus = 0.05
-
-        # Height and posture
         height = float(self.Player.world.global_position[2])
-        if 0.8 <= height <= 1.05:
-            height_reward = 1.0
-        elif 0.40 <= height <= 1.20:
-            height_reward = -1.0
-        else:
-            height_reward = -6.0
 
         orientation_quat_inv = R.from_quat(self.Player.robot._global_cheat_orientation).inv()
         projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
         tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
-        posture_penalty = -2.2 * (tilt_mag ** 2)
+        ang_vel = np.deg2rad(self.Player.robot.gyroscope)
+        ang_vel_mag = float(np.linalg.norm(ang_vel))
 
-        motionless_penalty = -1.5 if speed < 0.1 else 0.0
+        is_fallen = height < 0.3
+        if is_fallen:
+            # remain = max(0, 800 - self.step_counter)
+            # return -8.0 - 0.01 * remain
+            return -1.0
+        
 
-        # Waypoint bonus
-        waypoint_bonus = 0.0
-        if distance_to_target < 0.5:
-            waypoint_bonus = 25.0
-            if self.waypoint_index < len(self.point_list) - 1:
-                self.waypoint_index += 1
-                self.target_position = self.point_list[self.waypoint_index]
-            else:
-                waypoint_bonus = 100.0
-                self.route_completed = True
 
-        # Effort + smoothness
+        # # 目标方向
-        action_magnitude = float(np.linalg.norm(action[11:]))
+        # to_target = self.target_position - current_pos
-        action_penalty = -0.05 * action_magnitude
+        # dist_to_target = float(np.linalg.norm(to_target))
-        action_delta = action - self.last_action_for_reward
+        # if dist_to_target < 0.5:
-        smoothness_penalty = -0.02 * float(np.linalg.norm(action_delta[11:]))
+        #     return 15.0
+
+        # forward_dir = to_target / dist_to_target if dist_to_target > 0.1 else np.array([1.0, 0.0])
+        # delta_pos = current_pos - previous_pos
+        # forward_step = float(np.dot(delta_pos, forward_dir))
+        # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step))
+
+        # 奖励项
+        # progress_reward = 2 * forward_step
+        # lateral_penalty = -0.1 * lateral_step
+        alive_bonus = 2.0
+
+        # action_penalty = -0.01 * float(np.linalg.norm(action))
+        smoothness_penalty = -0.01 * float(np.linalg.norm(action - self.last_action_for_reward))
+
+        posture_penalty = -0.3 * (tilt_mag)
+        ang_vel_penalty = -0.02 * ang_vel_mag
+
+        target_height = self.initial_height
+        height_error =  height - target_height
+        height_penalty = -0.5 * abs(height_error)  # 惩罚高度偏离，系数可调
+
+        # # 在 compute_reward 开头附近，添加高度变化率计算
+        # if not hasattr(self, 'last_height'):
+        #     self.last_height = height
+        #     self.last_height_time = self.step_counter  # 可选，用于时间间隔
+        # height_rate = height - self.last_height  # 正为上升，负为下降
+        # self.last_height = height
+
+        # 惩罚高度下降（负变化率）
+        # height_down_penalty = -5.0 * max(0, -height_rate)  # 系数可调，-height_rate 为正表示下降幅度
+
+        # # 在 compute_reward 中
+        # if self.step_counter > 50:
+        #     avg_prev_action = np.mean(self.prev_action_history, axis=0)
+        #     novelty = float(np.linalg.norm(action - avg_prev_action))
+        #     exploration_bonus = 0.05 * novelty
+        # else:
+        #     exploration_bonus = 0
+
+        # self.prev_action_history[self.history_idx] = action
+        # self.history_idx = (self.history_idx + 1) % 50
+
+
+        total = (
+            # progress_reward  + 
+                 alive_bonus + 
+                 # lateral_penalty +
+                 # action_penalty +
+                smoothness_penalty +
+                 posture_penalty
+                 + ang_vel_penalty
+                 + height_penalty
+                #  + exploration_bonus
+                # + height_down_penalty
+                 )
+        if time.time() - self.start_time >= 1200:
+            self.start_time = time.time()
+            print(
+                #   f"progress_reward:{progress_reward:.4f}",
+                #   f"lateral_penalty:{lateral_penalty:.4f}",
+                #   f"action_penalty:{action_penalty:.4f}"s, 
+                  f"height_penalty:{height_penalty:.4f}",
+                  f"smoothness_penalty:{smoothness_penalty:.4f},",
+                  f"posture_penalty:{posture_penalty:.4f}",
+                #   f"ang_vel_penalty:{ang_vel_penalty:.4f}",
+                #   f"height_down_penalty:{height_down_penalty:.4f}",
+                #   f"exploration_bonus:{exploration_bonus:.4f}"
+            )
+
+        return total
+
 
-        return (
-                progress_reward
-                + speed_reward
-                + lateral_penalty
-                + direction_reward
-                + alive_bonus
-                + height_reward
-                + posture_penalty
-                + motionless_penalty
-                + waypoint_bonus
-                + action_penalty
-                + smoothness_penalty
-        )
 
     def step(self, action):
 
@@ -484,21 +466,23 @@ class WalkEnv(gym.Env):
         self.previous_action = action
 
         self.target_joint_positions = (
-                self.joint_nominal_position
+                # self.joint_nominal_position +
-                + self.scaling_factor * action
+                 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=40, kd=1.0
             )
 
+        self.previous_action = action
+
         self.sync()  # run simulation step
         self.step_counter += 1
 
-        # if self.step_counter % self.debug_every_n_steps == 0:
+        if self.enable_debug_joint_status and self.step_counter % self.debug_every_n_steps == 0:
-        #     self.debug_joint_status()
+            self.debug_joint_status()
 
         current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
 
@@ -529,10 +513,10 @@ class Train(Train_Base):
         n_envs = 20  # 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)
+        n_steps_per_env = 256  # RolloutBuffer is of size (n_steps_per_env * n_envs)
-        minibatch_size = 128  # should be a factor of (n_steps_per_env * n_envs)
+        minibatch_size = 512  # should be a factor of (n_steps_per_env * n_envs)
         total_steps = 30000000
-        learning_rate = 2e-4
+        learning_rate = 1e-4
         folder_name = f'Walk_R{self.robot_type}'
         model_path = f'./scripts/gyms/logs/{folder_name}/'
 
@@ -580,10 +564,12 @@ class Train(Train_Base):
                     learning_rate=learning_rate,
                     device="cpu",
                     policy_kwargs=policy_kwargs,
-                    # ent_coef=0.01,  # Entropy coefficient for exploration
+                    ent_coef=0.03,  # Entropy coefficient for exploration
-                    # clip_range=0.2,  # PPO clipping parameter
+                    clip_range=0.13,  # PPO clipping parameter
-                    # gae_lambda=0.95,  # GAE lambda
+                    gae_lambda=0.95,  # GAE lambda
-                    # gamma=0.99  # Discount factor
+                    gamma=0.95 , # Discount factor
+                    target_kl=0.03,
+                    n_epochs=5
                 )
 
             model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
@@ -635,6 +621,6 @@ if __name__ == "__main__":
     )
 
     trainer = Train(script_args)
-    trainer.train({})
+    trainer.train({"model_file": "scripts/gyms/logs/Walk_R0_004/best_model.zip"})
-    # trainer.test({"model_file": "scripts/gyms/logs/Walk_R0_003/best_model.zip",
+    # trainer.test({"model_file": "scripts/gyms/logs/Walk_R0_004/best_model.zip",
-    #               "folder_dir": "Walk_R0_003",})
+                #   "folder_dir": "scripts/gyms/logs/Walk_R0_004/",})

world/world.py

@@ -47,6 +47,7 @@ class World:
         self.their_team_players: list[OtherRobot] = [OtherRobot(is_teammate=False) for _ in
                                                      range(self.MAX_PLAYERS_PER_TEAM)]
         self.field: Field = self.__initialize_field(field_name=field_name)
+        self.WORLD_STEPTIME: float = 0.005  # Time step of the world in seconds
 
     def update(self) -> None:
         """