--- a
+++ b/nips/round2_course.py
@@ -0,0 +1,125 @@
+
+
+def checkpoints_0(self):
+    state_desc = self.get_state_desc()
+    prev_state_desc = self.get_prev_state_desc()
+    if not prev_state_desc:
+        return 0
+
+    reward = 2
+
+    pelvis = state_desc["body_pos"]["pelvis"][1]
+    reward -= max(0, 0.70 - pelvis) * 20
+
+    penalty = 0
+    # Small penalty for too much activation (cost of transport)
+    penalty += np.sum(np.array(self.osim_model.get_activations()) ** 2) * 0.001
+    # Big penalty for not matching the vector on the X,Z projection.
+    # No penalty for the vertical axis
+    penalty += abs(state_desc["body_vel"]["pelvis"][0] - state_desc["target_vel"][0]) * 2
+    penalty += abs(state_desc["body_vel"]["pelvis"][2] - state_desc["target_vel"][2]) * 2
+
+    reward -= penalty
+
+    return reward * 0.5
+
+
+# 接checkpoints_0
+def checkpoints_1(self):
+    state_desc = self.get_state_desc()
+    prev_state_desc = self.get_prev_state_desc()
+    if not prev_state_desc:
+        return 0
+
+    reward = 2 + state_desc["body_vel"]["pelvis"][0]
+
+    pelvis = state_desc["body_pos"]["pelvis"][1]
+    reward -= max(0, 0.70 - pelvis) * 20
+
+    penalty = 0
+    # Small penalty for too much activation (cost of transport)
+    penalty += np.sum(np.array(self.osim_model.get_activations()) ** 2) * 0.001
+    # Big penalty for not matching the vector on the X,Z projection.
+    # No penalty for the vertical axis
+    penalty += abs(state_desc["body_vel"]["pelvis"][0] - state_desc["target_vel"][0]) * 2
+    penalty += abs(state_desc["body_vel"]["pelvis"][2] - state_desc["target_vel"][2]) * 2
+
+    reward -= penalty
+
+    return reward * 0.5
+
+
+# 接checkpoints_1
+def checkpoints_2(self):
+    state_desc = self.get_state_desc()
+    prev_state_desc = self.get_prev_state_desc()
+    if not prev_state_desc:
+        return 0
+
+    reward = 2
+
+    pelvis = state_desc["body_pos"]["pelvis"][1]
+    reward -= max(0, 0.70 - pelvis) * 20
+
+    penalty = 0
+    # Small penalty for too much activation (cost of transport)
+    penalty += np.sum(np.array(self.osim_model.get_activations()) ** 2) * 0.001
+    # Big penalty for not matching the vector on the X,Z projection.
+    # No penalty for the vertical axis
+    penalty += abs(state_desc["body_vel"]["pelvis"][0] - state_desc["target_vel"][0]) * 2
+    penalty += abs(state_desc["body_vel"]["pelvis"][2] - state_desc["target_vel"][2]) * 2
+
+    reward -= penalty
+
+    return reward * 0.5
+
+
+# 接checkpoints_0
+def checkpoints_3(self):
+    state_desc = self.get_state_desc()
+    prev_state_desc = self.get_prev_state_desc()
+    if not prev_state_desc:
+        return 0
+
+    reward = 3
+
+    pelvis = state_desc["body_pos"]["pelvis"][1]
+    reward -= max(0, 0.70 - pelvis) * 20
+
+    penalty = 0
+    # Small penalty for too much activation (cost of transport)
+    penalty += np.sum(np.array(self.osim_model.get_activations()) ** 2) * 0.001
+    # Big penalty for not matching the vector on the X,Z projection.
+    # No penalty for the vertical axis
+    penalty += abs(state_desc["body_vel"]["pelvis"][0] - state_desc["target_vel"][0]) * 2
+    penalty += abs(state_desc["body_vel"]["pelvis"][2] - state_desc["target_vel"][2]) * 2
+
+    reward -= penalty
+
+    return reward * 0.5
+
+
+# 接checkpoints_3
+def checkpoints_4(self):
+    state_desc = self.get_state_desc()
+    prev_state_desc = self.get_prev_state_desc()
+    if not prev_state_desc:
+        return 0
+
+    reward = 2
+
+    pelvis = state_desc["body_pos"]["pelvis"][1]
+    reward -= max(0, 0.70 - pelvis) * 20
+
+    penalty = 0
+    # Small penalty for too much activation (cost of transport)
+    penalty += np.sum(np.array(self.osim_model.get_activations()) ** 2) * 0.001
+    # Big penalty for not matching the vector on the X,Z projection.
+    # No penalty for the vertical axis
+    penalty += abs(state_desc["body_vel"]["pelvis"][0] - state_desc["target_vel"][0]) * 2
+    penalty += abs(state_desc["body_vel"]["pelvis"][2] - state_desc["target_vel"][2]) * 2
+
+    reward -= penalty
+
+    return reward * 0.5
+