From 0501c4bb46a497d0e67ee815a3f222ba96b050ab Mon Sep 17 00:00:00 2001
From: MagDish <2717360869@qq.com>
Date: Sun, 24 Nov 2024 19:10:23 +0800
Subject: [PATCH] new best

---
 scripts/gyms/Sprint.py      | 15 +++++++--------
 scripts/gyms/sprint_demo.py |  4 ++--
 2 files changed, 9 insertions(+), 10 deletions(-)

diff --git a/scripts/gyms/Sprint.py b/scripts/gyms/Sprint.py
index 54642b8..951befc 100644
--- a/scripts/gyms/Sprint.py
+++ b/scripts/gyms/Sprint.py
@@ -43,8 +43,8 @@ class sprint(gym.Env):
         self.ik = self.player.inv_kinematics
 
         # Step behavior defaults
-        self.STEP_DUR = 10
-        self.STEP_Z_SPAN = 0.2
+        self.STEP_DUR = 8
+        self.STEP_Z_SPAN = 0.3
         self.STEP_Z_MAX = 0.7
         nao_specs = self.ik.NAO_SPECS
         self.leg_length = nao_specs[1] + nao_specs[3]  # upper leg height + lower leg height
@@ -56,7 +56,6 @@ class sprint(gym.Env):
 
         self.walk_rel_orientation = None
         self.walk_rel_target = None
-        self.walk_target = None
         self.walk_distance = None
         self.act = np.zeros(16, np.float32)  # memory variable
 
@@ -146,7 +145,7 @@ class sprint(gym.Env):
         Create internal target with a smoother variation
         '''
 
-        MAX_LINEAR_DIST = 0.5
+        MAX_LINEAR_DIST = 1
         MAX_LINEAR_DIFF = 0.014  # max difference (meters) per step
         MAX_ROTATION_DIFF = 1.6  # max difference (degrees) per step
         MAX_ROTATION_DIST = 45
@@ -192,7 +191,6 @@ class sprint(gym.Env):
         self.obs[62] = internal_target_vel[0] / MAX_LINEAR_DIFF
 
         return self.obs
-
     def execute_ik(self, l_pos, l_rot, r_pos, r_rot):
         r = self.player.world.robot
         # Apply IK to each leg + Set joint targets
@@ -311,7 +309,7 @@ class sprint(gym.Env):
             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 = 0.6 * self.act + 0.4 * action
+        self.act = 0.8 * self.act + 0.2 * action * 0.7
 
         # execute Step behavior to extract the target positions of each leg (we will override these targets)
         lfy, lfz, rfy, rfz = self.step_generator.get_target_positions(self.step_counter == 0, self.STEP_DUR,
@@ -341,16 +339,17 @@ class sprint(gym.Env):
         self.sync()
         self.step_counter += 1
         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, 10)
-        reward = robot_speed * (1 - direction_error / 10) * 0.02
+        reward = robot_speed * (1.5 - direction_error / 10) * 0.03
         if self.walk_distance < 0.3:
             reward += 10
 
         if self.player.behavior.is_ready("Get_Up"):
             self.terminal = True
-        elif w.time_local_ms - self.reset_time > 15000 * 2:
+        elif w.time_local_ms - self.reset_time > 45000 * 2:
             self.terminal = True
         else:
             self.terminal = False
diff --git a/scripts/gyms/sprint_demo.py b/scripts/gyms/sprint_demo.py
index 87a8676..5bc0911 100644
--- a/scripts/gyms/sprint_demo.py
+++ b/scripts/gyms/sprint_demo.py
@@ -45,7 +45,7 @@ class sprint(gym.Env):
         # Step behavior defaults
         self.STEP_DUR = 8
         self.STEP_Z_SPAN = 0.3
-        self.STEP_Z_MAX = 0.8
+        self.STEP_Z_MAX = 0.7
         nao_specs = self.ik.NAO_SPECS
         self.leg_length = nao_specs[1] + nao_specs[3]  # upper leg height + lower leg height
         feet_y_dev = nao_specs[0] * 2  # wider step
@@ -310,7 +310,7 @@ class sprint(gym.Env):
         self.walk_rel_orientation = M.vector_angle(self.walk_rel_target) * 0.3
 
         # exponential moving average
-        self.act = 0.8 * self.act + 0.2 * 0.7
+        self.act = 0.8 * self.act + 0.2 * action_mult * 0.7
 
         # execute Step behavior to extract the target positions of each leg (we will override these targets)
         lfy, lfz, rfy, rfz = self.step_generator.get_target_positions(self.step_counter == 0, self.STEP_DUR,