From d7ca7b6b99cb4c58b3fb1a6878c422cfdd77596c Mon Sep 17 00:00:00 2001
From: MagDish <2717360869@qq.com>
Date: Thu, 14 Nov 2024 16:19:33 +0800
Subject: [PATCH] new

---
 scripts/gyms/sprint.py | 28 +++++++++++++++-------------
 1 file changed, 15 insertions(+), 13 deletions(-)

diff --git a/scripts/gyms/sprint.py b/scripts/gyms/sprint.py
index 210fc31..43cc2a2 100644
--- a/scripts/gyms/sprint.py
+++ b/scripts/gyms/sprint.py
@@ -283,14 +283,13 @@ class sprint(gym.Env):
         self.player.terminate()
 
 
-    def generate_random_target(self, x_range=(-12, 12), y_range=(-9, 9)):
+    def generate_random_target(self, x_range=(-15, 15), y_range=(-10, 10)):
+        r = self.player.world.robot
         while True:
             x = np.random.uniform(x_range[0], x_range[1])
             y = np.random.uniform(y_range[0], y_range[1])
 
-            if np.linalg.norm(np.array([x, y]) - self.Gen_player_pos[:2]) >= 10:
-                if abs(x) > 15 or abs(y) > 10:
-                    continue
+            if np.linalg.norm(np.array([x, y]) - r.loc_head_position[:2]) >= 15:
                 break
 
         self.walk_target = np.array([x, y])
@@ -300,14 +299,18 @@ class sprint(gym.Env):
              player.world.robot)
 
         w = self.player.world
-
         internal_dist = np.linalg.norm(self.internal_target)
         action_mult = 1 if internal_dist > 0.2 else (0.7 / 0.2) * internal_dist + 0.3
         self.walk_rel_target = M.rotate_2d_vec(
             (self.walk_target[0] - r.loc_head_position[0], self.walk_target[1] - r.loc_head_position[1]), -r.imu_torso_orientation)
-        self.walk_distance = np.linalg.norm(self.walk_target[:2] - self.Gen_player_pos[:2])
+        self.walk_distance = np.linalg.norm(self.walk_target[:2] - r.loc_head_position[:2])
+        if self.walk_distance < 0.5:
+            self.generate_random_target()
+            self.walk_rel_target = M.rotate_2d_vec(
+                (self.walk_target[0] - r.loc_head_position[0], self.walk_target[1] - r.loc_head_position[1]),
+                -r.imu_torso_orientation)
+            self.walk_distance = np.linalg.norm(self.walk_target[:2] - r.loc_head_position[:2])
         self.walk_rel_orientation = M.vector_angle(self.walk_rel_target) * 0.3
-
         # exponential moving average
         self.act = 0.8 * self.act + 0.2 * action * action_mult * 0.7
 
@@ -339,17 +342,16 @@ class sprint(gym.Env):
         self.sync()
         self.step_counter += 1
         obs = self.observe()
-        robot_speed = r.loc_torso_velocity[0]
+        robot_speed = np.linalg.norm(r.loc_torso_velocity[:2])
         direction_error = abs(self.walk_rel_orientation)
-        direction_error = min(direction_error, 10)
-        reward = robot_speed * (1.5 - direction_error / 10)
-        if self.walk_distance < 0.5:
+        direction_error = min(direction_error, 20)
+        reward = robot_speed * (1 - direction_error / 20)
+        if self.walk_distance < 0.8:
             reward += 10
-            self.generate_random_target()
 
         if self.player.behavior.is_ready("Get_Up"):
             self.terminal = True
-        elif w.time_local_ms - self.reset_time > 100000:
+        elif w.time_local_ms - self.reset_time > 300000:
             self.terminal = True
         else:
             self.terminal = False