Successful, not fined tuned HoverAviary training

utiasDSL · Nov 26, 2023 · 2348440 · 2348440
1 parent 0407760
commit 2348440
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Showing 4 changed files with 9 additions and 4 deletions.
diff --git a/gym_pybullet_drones/envs/BaseRLAviary.py b/gym_pybullet_drones/envs/BaseRLAviary.py
@@ -69,7 +69,6 @@ def __init__(self,
         vision_attributes = True if obs == ObservationType.RGB else False
         self.OBS_TYPE = obs
         self.ACT_TYPE = act
-        self.EPISODE_LEN_SEC = 5
         #### Create integrated controllers #########################
         if act in [ActionType.PID, ActionType.VEL, ActionType.ONE_D_PID]:
             os.environ['KMP_DUPLICATE_LIB_OK']='True'

diff --git a/gym_pybullet_drones/envs/HoverAviary.py b/gym_pybullet_drones/envs/HoverAviary.py
@@ -49,6 +49,7 @@ def __init__(self,
 
         """
         self.TARGET_POS = np.array([0,0,1])
+        self.EPISODE_LEN_SEC = 8
         super().__init__(drone_model=drone_model,
                          num_drones=1,
                          initial_xyzs=initial_xyzs,
@@ -74,7 +75,7 @@ def _computeReward(self):
 
         """
         state = self._getDroneStateVector(0)
-        ret = max(0, 10 - np.linalg.norm(self.TARGET_POS-state[0:3])**4)
+        ret = max(0, 2 - np.linalg.norm(self.TARGET_POS-state[0:3])**4)
         return ret
 
     ################################################################################

diff --git a/gym_pybullet_drones/envs/LeaderFollowerAviary.py b/gym_pybullet_drones/envs/LeaderFollowerAviary.py
@@ -55,6 +55,7 @@ def __init__(self,
 
         """
         self.TARGET_POS = np.array([0,0,1])
+        self.EPISODE_LEN_SEC = 5
         super().__init__(drone_model=drone_model,
                          num_drones=num_drones,
                          neighbourhood_radius=neighbourhood_radius,

diff --git a/gym_pybullet_drones/examples/learn.py b/gym_pybullet_drones/examples/learn.py
@@ -85,7 +85,11 @@ def run(multiagent=DEFAULT_MA, output_folder=DEFAULT_OUTPUT_FOLDER, gui=DEFAULT_
                                  eval_freq=int(2000),
                                  deterministic=True,
                                  render=False)
-    model.learn(total_timesteps=int(1e6),
+    if not multiagent:
+        steps = 2 * int(1e5)
+    else:
+        steps = int(1e6)
+    model.learn(total_timesteps=steps,
                 callback=eval_callback,
                 log_interval=100)
 
@@ -140,7 +144,7 @@ def run(multiagent=DEFAULT_MA, output_folder=DEFAULT_OUTPUT_FOLDER, gui=DEFAULT_
 
     obs, info = test_env.reset(seed=42, options={})
     start = time.time()
-    for i in range(3*test_env.CTRL_FREQ):
+    for i in range((test_env.EPISODE_LEN_SEC+2)*test_env.CTRL_FREQ):
         action, _states = model.predict(obs,
                                         deterministic=True
                                         )