[wip,broken,debug] Investigate slow performance on reset

Author: amacati

benchmarks/sim.py

@@ -77,7 +77,7 @@
 import lsy_drone_racing
 
 env = gymnasium.make('MultiDroneRacing-v0',
-    n_envs=1,  # TODO: Remove this for single-world envs
+    n_envs=1000,  # TODO: Remove this for single-world envs
     n_drones=config.env.n_drones,
     freq=config.env.freq,
     sim_config=config.sim,
@@ -87,6 +87,7 @@
     randomizations=config.env.get("randomizations"),
     random_resets=config.env.random_resets,
     seed=config.env.seed,
+    device='gpu',
 )
 env.reset()
 env.step(env.action_space.sample())  # JIT compile

config/multi_level0.toml

@@ -27,15 +27,6 @@ freq = 500                                        # Simulation frequency, in Hz
 attitude_freq = 500                               # Controller frequency, in Hz. This frequency is used to simulate the onboard controller, NOT for the environment's step function
 gui = false                                       # Enable/disable PyBullet's GUI
 
-[sim.disturbances.action]
-type = "GaussianNoise"
-std = 0.001
-
-[sim.disturbances.dynamics]
-type = "UniformNoise"
-low = [-0.1, -0.1, -0.1]
-high = [0.1, 0.1, 0.1]
-
 [env]
 id = "MultiDroneRacing-v0" # Either "MultiDroneRacingEnv-v0" or "MultiDroneRacingAttitude-v0". If using "MultiDroneRacingAttitude-v0", the drone will use the attitude controller instead of the position controller.
 n_drones = 2          # Number of drones has to match the track configuration for drones

config/multi_level3.toml

@@ -0,0 +1,121 @@
+# Level 0
+
+# | Evaluation Scenario | Rand. Inertial Properties | Randomized Obstacles, Gates | Rand. Between Episodes | Notes             |
+# | :-----------------: | :-----------------------: | :-------------------------: | :--------------------: | :---------------: |
+# | `level0.toml`       | *No*                      | *No*                        | *No*                   | Perfect knowledge |
+[controller]
+file = "trajectory_controller.py" # Put your controller file name here. Specifying a controller as argument to scripts will override this setting. Controllers are located in `lsy_drone_racing/control/`
+
+[deploy]
+### Settings only relevant for deployment
+# Whether to check if gate and obstacle positions observed by vicon are within the limits defined down below.
+check_race_track = true
+# Whether to check if the drone start position is within the limits specified down below.
+check_drone_start_pos = true
+# Lets you practice your controller without putting up gates & obstacles, assumes nominal positions given below.
+practice_without_track_objects = false
+
+[sim]
+# Physics options:
+# "analytical": Analytical, simplified dynamics model
+# "mujoco": Mujoco dynamics. May take longer to compile at startup.
+# "sys_id": System identification model.
+physics = "analytical"
+
+camera_view = [5.0, -40.0, -40.0, 0.5, -1.0, 0.5]
+freq = 500                                        # Simulation frequency, in Hz
+attitude_freq = 500                               # Controller frequency, in Hz. This frequency is used to simulate the onboard controller, NOT for the environment's step function
+gui = false                                       # Enable/disable PyBullet's GUI
+
+[env]
+id = "MultiDroneRacing-v0" # Either "MultiDroneRacingEnv-v0" or "MultiDroneRacingAttitude-v0". If using "MultiDroneRacingAttitude-v0", the drone will use the attitude controller instead of the position controller.
+n_drones = 2          # Number of drones has to match the track configuration for drones
+random_resets = true  # Whether to re-seed the random number generator between episodes
+seed = 1337           # Random seed
+freq = 50             # Frequency of the environment's step function, in Hz
+symbolic = false      # Whether to include symbolic expressions in the info dict. Note: This can interfere with multiprocessing! If you want to parallelize your training, set this to false.
+sensor_range = 0.45   # Range at which the exact location of gates and obstacles become visible to the drone. Objects that are not in the drone's sensor range report their nominal position.
+
+[env.track]
+# Tall gates: 1.0m height. Short gates: 0.525m height. Height is measured from the ground to the
+# center of the gate.
+[[env.track.gates]]
+pos = [0.45, -1.0, 0.56]
+rpy = [0.0, 0.0, 2.35]
+[[env.track.gates]]
+pos = [1.0, -1.55, 1.11]
+rpy = [0.0, 0.0, -0.78]
+[[env.track.gates]]
+pos = [0.0, 0.5, 0.56]
+rpy = [0.0, 0.0, 0.0]
+[[env.track.gates]]
+pos = [-0.5, -0.5, 1.11]
+rpy = [0.0, 0.0, 3.14]
+
+# Obstacle height: 1.4m. Height is measured from the ground to the top of the obstacle.
+[[env.track.obstacles]]
+pos = [1.0, -0.5, 1.4]
+[[env.track.obstacles]]
+pos = [0.5, -1.5, 1.4]
+[[env.track.obstacles]]
+pos = [-0.5, 0.0, 1.4]
+[[env.track.obstacles]]
+pos = [0.0, 1.0, 1.4]
+
+[env.track.drone]
+pos = [[1.0, 1.0, 0.05], [1.2, 1.0, 0.05]]
+rpy = [[0, 0, 0], [0, 0, 0]]
+vel = [[0, 0, 0], [0, 0, 0]]
+rpy_rates = [[0, 0, 0], [0, 0, 0]]
+
+[env.disturbances.action]
+fn = "normal"
+scale = 0.001
+
+[env.disturbances.dynamics]
+fn = "uniform"
+[env.disturbances.dynamics.kwargs]
+minval = [-0.1, -0.1, -0.1]
+maxval = [0.1, 0.1, 0.1]
+
+[env.randomizations.drone_pos]
+fn = "uniform"
+[env.randomizations.drone_pos.kwargs]
+minval = [-0.1, -0.1, 0.0]
+maxval = [0.1, 0.1, 0.02]
+
+[env.randomizations.drone_rpy]
+fn = "uniform"
+[env.randomizations.drone_rpy.kwargs]
+minval = [-0.1, -0.1, -0.1]
+maxval = [0.1, 0.1, 0.1]
+
+[env.randomizations.drone_mass]
+fn = "uniform"
+[env.randomizations.drone_mass.kwargs]
+minval = -0.01
+maxval = 0.01
+
+[env.randomizations.drone_inertia]
+fn = "uniform"
+[env.randomizations.drone_inertia.kwargs]
+minval = [-0.000001, -0.000001, -0.000001]
+maxval = [0.000001, 0.000001, 0.000001]
+
+[env.randomizations.gate_pos]
+fn = "uniform"
+[env.randomizations.gate_pos.kwargs]
+minval = [-0.15, -0.15, -0.1]
+maxval = [0.15, 0.15, 0.1]
+
+[env.randomizations.gate_rpy]
+fn = "uniform"
+[env.randomizations.gate_rpy.kwargs]
+minval = [0.0, 0.0, -0.1]
+maxval = [0.0, 0.0, 0.1]
+
+[env.randomizations.obstacle_pos]
+fn = "uniform"
+[env.randomizations.obstacle_pos.kwargs]
+minval = [-0.15, -0.15, -0.05]
+maxval = [0.15, 0.15, 0.05]