new

scripts/gyms/sprint.py

@@ -233,7 +233,7 @@ class sprint(gym.Env):
         w = self.player.world
         t = w.time_local_ms
         self.reset_time = t
-        self.generate_random_target()
+        self.generate_random_target(self.Gen_player_pos[:2])
         distance = np.linalg.norm(self.walk_target[:2] - self.Gen_player_pos[:2])
         self.walk_distance = distance
         self.walk_rel_target = M.rotate_2d_vec(
@@ -283,13 +283,12 @@ class sprint(gym.Env):
         self.player.terminate()
 
 
-    def generate_random_target(self, x_range=(-15, 15), y_range=(-10, 10)):
+    def generate_random_target(self, position, 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]) - r.loc_head_position[:2]) >= 15:
+            if np.linalg.norm(np.array([x, y]) - position) >= 10:
                 break
 
         self.walk_target = np.array([x, y])
@@ -303,13 +302,13 @@ class sprint(gym.Env):
         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] - r.loc_head_position[:2])
+        self.walk_distance = np.linalg.norm(self.walk_target - r.loc_head_position[:2])
-        if self.walk_distance < 0.5:
+        if self.walk_distance <= 0.5:
-            self.generate_random_target()
+            self.generate_random_target(r.loc_head_position[:2])
             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_distance = np.linalg.norm(self.walk_target - 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
@@ -344,15 +343,13 @@ class sprint(gym.Env):
         obs = self.observe()
         robot_speed = np.linalg.norm(r.loc_torso_velocity[:2])
         direction_error = abs(self.walk_rel_orientation)
-        direction_error = min(direction_error, 20)
+        direction_error = min(direction_error, 10)
-        reward = robot_speed * (1 - direction_error / 20)
+        reward = robot_speed * (1 - direction_error / 10) * 0.6
-        if self.walk_distance < 0.8:
+        if self.walk_distance < 0.5:
             reward += 10
 
         if self.player.behavior.is_ready("Get_Up"):
             self.terminal = True
-        elif w.time_local_ms - self.reset_time > 300000:
-            self.terminal = True
         else:
             self.terminal = False
         return obs, reward, self.terminal, {}