new gifs, trained examples

README.md

@@ -4,7 +4,7 @@ This is a minimalist refactoring of the original `gym-pybullet-drones` repositor
 
 > **NOTE**: if you prefer to access the original codebase, presented at IROS in 2021, please `git checkout [paper|master]` after cloning the repo, and refer to the corresponding `README.md`'s.
 
-<img src="gym_pybullet_drones/assets/helix.gif" alt="formation flight" width="350"> <img src="gym_pybullet_drones/assets/helix.png" alt="control info" width="450">
+<img src="gym_pybullet_drones/assets/helix.gif" alt="formation flight" width="325"> <img src="gym_pybullet_drones/assets/helix.png" alt="control info" width="425">
 
 ## Installation
 
@@ -39,14 +39,15 @@ cd gym_pybullet_drones/examples/
 python3 downwash.py
 ```
 
-### Reinforcement learning 10'-training example (SB3's PPO)
+### Reinforcement learning examples (SB3's PPO)
 
 ```sh
 cd gym_pybullet_drones/examples/
 python learn.py # task: single drone hover at z == 1.0
+python learn.py --multiagent true # task: 2-drone hover at z == 1.2 and 0.7
 ```
 
-<img src="gym_pybullet_drones/assets/rl.gif" alt="rl example" width="800">
+<img src="gym_pybullet_drones/assets/rl.gif" alt="rl example" width="375"> <img src="gym_pybullet_drones/assets/marl.gif" alt="marl example" width="375">
 
 ### Betaflight SITL example (Ubuntu only)
 

gym_pybullet_drones/envs/MultiHoverAviary.py

@@ -68,7 +68,6 @@ def __init__(self,
                          obs=obs,
                          act=act
                          )
-
         self.TARGET_POS = self.INIT_XYZS + np.array([[0,0,1/(i+1)] for i in range(num_drones)])
 
     ################################################################################
@@ -119,6 +118,12 @@ def _computeTruncated(self):
             Whether the current episode timed out.
 
         """
+        states = np.array([self._getDroneStateVector(i) for i in range(self.NUM_DRONES)])
+        for i in range(self.NUM_DRONES):
+            if (abs(states[i][0]) > 2.0 or abs(states[i][1]) > 2.0 or states[i][2] > 2.0 # Truncate when a drones is too far away
+             or abs(states[i][7]) > .5 or abs(states[i][8]) > .5 # Truncate when a drone is too tilted
+            ):
+                return True
         if self.step_counter/self.PYB_FREQ > self.EPISODE_LEN_SEC:
             return True
         else:

gym_pybullet_drones/examples/learn.py

@@ -71,21 +71,20 @@ def run(multiagent=DEFAULT_MA, output_folder=DEFAULT_OUTPUT_FOLDER, gui=DEFAULT_
     #### Train the model #######################################
     model = PPO('MlpPolicy',
                 train_env,
-                # policy_kwargs=dict(activation_fn=torch.nn.ReLU, net_arch=[512, 512, dict(vf=[256, 128], pi=[256, 128])]),
                 # tensorboard_log=filename+'/tb/',
                 verbose=1)
 
-    callback_on_best = StopTrainingOnRewardThreshold(reward_threshold=np.inf,
+    callback_on_best = StopTrainingOnRewardThreshold(reward_threshold=465 if not multiagent else 920, # reward thresholds for the 3D case, use 474 and 950 for the 1D case
                                                      verbose=1)
     eval_callback = EvalCallback(eval_env,
                                  callback_on_new_best=callback_on_best,
                                  verbose=1,
                                  best_model_save_path=filename+'/',
                                  log_path=filename+'/',
-                                 eval_freq=int(2000),
+                                 eval_freq=int(1000),
                                  deterministic=True,
                                  render=False)
-    model.learn(total_timesteps=int(1e6) if local else int(1e2), # shorter training in GitHub Actions pytest
+    model.learn(total_timesteps=int(1e7) if local else int(1e2), # shorter training in GitHub Actions pytest
                 callback=eval_callback,
                 log_interval=100)
 
@@ -107,7 +106,9 @@ def run(multiagent=DEFAULT_MA, output_folder=DEFAULT_OUTPUT_FOLDER, gui=DEFAULT_
     if local:
         input("Press Enter to continue...")
 
-    if os.path.isfile(filename+'/best_model.zip'):
+    if os.path.isfile(filename+'/final_model.zip'):
+        path = filename+'/final_model.zip'
+    elif os.path.isfile(filename+'/best_model.zip'):
         path = filename+'/best_model.zip'
     else:
         print("[ERROR]: no model under the specified path", filename)