update api_test.py for 1.0.0

api_test.py

@@ -1,7 +1,8 @@
+#pip install CogEnvDecoder==1.0.0
 from Cogenvdecoder.CogEnvDecoder import CogEnvDecoder
 import numpy as np
 import cv2
-
+import time
 def check_state(state, info=None):
     image_data = state["color_image"]
     laser_data = np.array(state["laser"])
@@ -13,6 +14,8 @@ def check_state(state, info=None):
     # self_pose: [x, y, theta(rad)], self_info: [remaining HP, remaining bullet]
     # enemy_pose: [x, y, theta(rad)], enemy_info: [remaining HP, remaining bullet]
     print("self pose: {}, self info: {}, enemy active: {}, enemy pose: {}, enemy_info: {}".format(vector_data[0], vector_data[1], vector_data[2], vector_data[3], vector_data[4]))
+    # self_velocity: [vx, vy, vw]
+    print("self velocity: {}".format(vector_data[11]))
     # goal_x: [x, y, is_activated?]
     print("goal 1: {}, goal 2: {}, goal 3: {}, goal 4: {}, goal 5:{}".format(vector_data[5], vector_data[6], vector_data[7], vector_data[8], vector_data[9]))
     # total counts of collisions, total collision time
@@ -24,23 +27,33 @@ def check_state(state, info=None):
     print("-----------------------end check---------------------")
 
 
-env = CogEnvDecoder(env_name="linux_V1/1.x86_64", no_graphics=False, time_scale=1, worker_id=1) # linux os
+env = CogEnvDecoder(env_name="stage2/reality_linux_v3.0/cog_sim2real_env.x86_64", no_graphics=False, 
+                    time_scale=1, worker_id=2, seed=1234, force_sync=True) # linux os
 # env = CogEnvDecoder(env_name="win_V1/RealGame.exe", no_graphics=False, time_scale=1, worker_id=1) # windows os
 # env_name: path of the simulator
 # no_graphics: should use headless mode [Warning: if no_graphics is True, image if invalid!]
 # time_scale: useful for speedup collecting data during training, max value is 100
 # worker_id: socket port offset, useful for multi-thread training
+# seed: random seed for generate position of the goals and the robots
+# force_sync: use time synchronization (True) or not (False)
 num_episodes = 10
 num_steps_per_episode = 500 # max: 1500
 for i in range(num_episodes):
     #every time call the env.reset() will reset the envinronment
-    observation = env.reset()
+    observation = env.reset(fri_cor=0.1, KP=8, KI=0, KD=2, VK1=0.375, M=3.4, Wheel_I=0.0125)
+    # fir_cor: friction factor
+    # KP, KI, KD: parameters of PID
+    # VK1: parameter of the motor(M3508I)
+    # M: the mass of the robot
+    # Wheel_I: the inertia of the wheel
 
     for j in range(num_steps_per_episode):
-        # action = env.action_space.sample()
-        action = [0.5, 0.5, 0.1, 0]  # [vx, vy, vw, fire]; vx: the velocity at which the vehicle moves forward, vy: the velocity at which the vehicle moves to the left, vw: Angular speed of the vehicle counterclockwise rotation, fire: Shoot or not
+        action = env.action_space.sample()
+        # action = [0.5, 0.5, 0.1, 0]  # [vx, vy, vw, fire]; vx: the velocity at which the vehicle moves forward, vy: the velocity at which the vehicle moves to the left, vw: Angular speed of the vehicle counterclockwise rotation, fire: Shoot or not
         obs, reward, done, info = env.step(action)
         cv2.imshow("color_image", obs["color_image"])
         cv2.waitKey(1)
         check_state(obs, info)
-        print(reward)
+        if done:
+            break
+