training Turn 0.1.0 version

scripts/gyms/Walk.py

@@ -153,7 +153,7 @@ class WalkEnv(gym.Env):
         self.min_stance_rad = 0.10
 
         # Small reset perturbations for robustness training.
-        self.enable_reset_perturb = True
+        self.enable_reset_perturb = False
         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 = 0.025
         self.reset_perturb_steps = 4
@@ -162,6 +162,7 @@ class WalkEnv(gym.Env):
         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
+        self.last_yaw_error = None
         self.Player.server.connect()
         # sleep(2.0)  # Longer wait for connection to establish completely
         self.Player.server.send_immediate(
@@ -204,6 +205,10 @@ class WalkEnv(gym.Env):
         except OSError:
             pass
 
+    @staticmethod
+    def _wrap_to_pi(angle_rad: float) -> float:
+        return (angle_rad + math.pi) % (2.0 * math.pi) - math.pi
+
     def observe(self, init=False):
 
         """获取当前观测值"""
@@ -312,11 +317,8 @@ class WalkEnv(gym.Env):
         if seed is not None:
             np.random.seed(seed)
 
-        length1 = 2  # randomize target distance
+        target_distance = np.random.uniform(1.2, 2.8)
-        length2 = np.random.uniform(0.6, 1)  # randomize target distance
+        target_bearing_deg = np.random.uniform(-180.0, 180.0)
-        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
 
         self.step_counter = 0
         self.waypoint_index = 0
@@ -324,6 +326,7 @@ class WalkEnv(gym.Env):
         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])  # Initialize for first step
+        self.last_yaw_error = None
         self.walk_cycle_step = 0
 
         # 随机 beam 目标位置和朝向，增加训练多样性
@@ -379,12 +382,14 @@ class WalkEnv(gym.Env):
         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)
-        # Build target in the robot's current forward direction instead of fixed global +x.
+        # Randomize global target bearing so policy must learn to rotate toward it first.
         heading_deg = float(r.global_orientation_euler[2])
-        forward_offset = MathOps.rotate_2d_vec(np.array([length1, 0.0]), heading_deg, is_rad=False)
+        target_offset = MathOps.rotate_2d_vec(
-        point1 = self.initial_position + forward_offset
+            np.array([target_distance, 0.0]),
-        point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False)
+            heading_deg + target_bearing_deg,
-        point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False)
+            is_rad=False,
+        )
+        point1 = self.initial_position + target_offset
         self.point_list = [point1]
         self.target_position = self.point_list[self.waypoint_index]
         self.initial_height = self.Player.world.global_position[2]
@@ -408,7 +413,7 @@ class WalkEnv(gym.Env):
         if is_fallen:
             # remain = max(0, 800 - self.step_counter)
             # return -8.0 - 0.01 * remain
-            return -1.0
+            return -2
         
 
 
@@ -423,16 +428,43 @@ class WalkEnv(gym.Env):
         # forward_step = float(np.dot(delta_pos, forward_dir))
         # lateral_step = float(np.linalg.norm(delta_pos - forward_dir * forward_step))
 
-        # 奖励项
+        alive_bonus = 0.5
-        # 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))
+        smoothness_penalty = -0.06 * float(np.linalg.norm(action - self.last_action_for_reward))
 
-        posture_penalty = -0.3 * (tilt_mag)
+        posture_penalty = -0.5 * (tilt_mag)
-        ang_vel_penalty = -0.02 * ang_vel_mag
+        ang_vel_penalty = -0.04 * ang_vel_mag
+
+        # Turn-to-target shaping.
+        to_target = self.target_position - current_pos
+        dist_to_target = float(np.linalg.norm(to_target))
+        if dist_to_target > 1e-6:
+            target_yaw = math.atan2(float(to_target[1]), float(to_target[0]))
+        else:
+            target_yaw = 0.0
+
+        robot_yaw = math.radians(float(robot.global_orientation_euler[2]))
+        yaw_error = self._wrap_to_pi(target_yaw - robot_yaw)
+
+        # Dense alignment reward in [-1, 1], max when facing target.
+        heading_align_reward = 1.6 * math.cos(yaw_error)
+
+        # Reward reducing heading error across consecutive control steps.
+        if self.last_yaw_error is None:
+            heading_progress_reward = 0.0
+        else:
+            heading_progress_reward = 1.2 * (abs(self.last_yaw_error) - abs(yaw_error))
+        self.last_yaw_error = yaw_error
+
+        # Encourage yaw rotation in the correct direction while far from alignment.
+        yaw_rate = float(np.deg2rad(robot.gyroscope[2]))
+        turn_dir = float(np.sign(yaw_error))
+        turn_cap = max(0.03, 0.08 * abs(yaw_error))
+        turn_rate_reward = float(np.clip(0.35 * turn_dir * yaw_rate, -turn_cap, turn_cap)) if abs(yaw_error) > math.radians(10.0) else 0.0
+
+        # Small bonus for holding a good heading; prevents oscillation near target angle.
+        heading_hold_bonus = 0.25 if abs(yaw_error) < math.radians(10.0) else 0.0
 
         # Use simulator joint readings in training frame to shape lateral stance.
         joint_pos = np.deg2rad(
@@ -455,16 +487,6 @@ class WalkEnv(gym.Env):
         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)
@@ -488,6 +510,10 @@ class WalkEnv(gym.Env):
                  + height_penalty
                  + stance_collapse_penalty
                  + cross_leg_penalty
+                 + heading_align_reward
+                 + heading_progress_reward
+                 + turn_rate_reward
+                 + heading_hold_bonus
                 #  + exploration_bonus
                 # + height_down_penalty
                  )
@@ -502,6 +528,10 @@ class WalkEnv(gym.Env):
                     f"posture_penalty:{posture_penalty:.4f}",
                     f"stance_collapse_penalty:{stance_collapse_penalty:.4f}",
                     f"cross_leg_penalty:{cross_leg_penalty:.4f}",
+                    f"heading_align_reward:{heading_align_reward:.4f}",
+                    f"heading_progress_reward:{heading_progress_reward:.4f}",
+                    f"turn_rate_reward:{turn_rate_reward:.4f}",
+                    f"heading_hold_bonus:{heading_hold_bonus:.4f}",
                 #   f"ang_vel_penalty:{ang_vel_penalty:.4f}",
                 #   f"height_down_penalty:{height_down_penalty:.4f}",
                 #   f"exploration_bonus:{exploration_bonus:.4f}"
@@ -569,7 +599,7 @@ class Train(Train_Base):
         minibatch_size = int(os.environ.get("GYM_CPU_TRAIN_BATCH_SIZE", "512"))  # should be a factor of (n_steps_per_env * n_envs)
         total_steps = 30000000
         learning_rate = float(os.environ.get("GYM_CPU_TRAIN_LR", "3e-4"))
-        folder_name = f'Walk_R{self.robot_type}'
+        folder_name = f'Turn_R{self.robot_type}'
         model_path = f'./scripts/gyms/logs/{folder_name}/'
 
         print(f"Model path: {model_path}")
@@ -596,7 +626,7 @@ class Train(Train_Base):
             sleep(server_warmup_sec)
         print("Servers started, creating environments...")
 
-        env = SubprocVecEnv([init_env(i, monitor=True) for i in range(n_envs)])
+        env = SubprocVecEnv([init_env(i, monitor=True) for i in range(n_envs)], start_method="spawn")
         # Use single-process eval env to avoid extra subprocess fragility during callback evaluation.
         eval_env = DummyVecEnv([init_env(n_envs, monitor=True)])
 
@@ -633,7 +663,7 @@ class Train(Train_Base):
                 )
 
             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, eval_eps=100,
+                                          eval_freq=n_steps_per_env * 20, save_freq=n_steps_per_env * 20, eval_eps=30,
                                           backup_env_file=__file__)
         except KeyboardInterrupt:
             sleep(1)  # wait for child processes
@@ -694,8 +724,8 @@ if __name__ == "__main__":
     run_mode = os.environ.get("GYM_CPU_MODE", "train").strip().lower()
 
     if run_mode == "test":
-        test_model_file = os.environ.get("GYM_CPU_TEST_MODEL", "scripts/gyms/logs/Walk_R0_004/best_model.zip")
+        test_model_file = os.environ.get("GYM_CPU_TEST_MODEL", "scripts/gyms/logs/Turn_R0_004/best_model.zip")
-        test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Walk_R0_004/")
+        test_folder = os.environ.get("GYM_CPU_TEST_FOLDER", "scripts/gyms/logs/Turn_R0_004/")
         trainer.test({"model_file": test_model_file, "folder_dir": test_folder})
     else:
         retrain_model = os.environ.get("GYM_CPU_TRAIN_MODEL", "").strip()