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nan values in networks #8

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JankowskiChristopher opened this issue Apr 13, 2024 · 1 comment
Open

nan values in networks #8

JankowskiChristopher opened this issue Apr 13, 2024 · 1 comment

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@JankowskiChristopher
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Hello,
When running the code on deepmind/pendulum-swingup the training crashes as the action becomes nan. I attach stack trace below (I added some more logging to catch exactly which part of the agent produces nan action, the original error was later when interacting with the environment, but the cause is here). I believe that more envs share this problem as in my previous runs I also experienced this - happened mostly for dog tasks, but as I was using my custom wrapper instead of shimmy I thought that maybe it had been some problem with my wrapper. Now it happens with shimmy so it is not the case of the wrapper but probably some instabilities (maybe with BatchNorm?).

237 Traceback (most recent call last):
 238   File "/home/src/crossq/train.py", line 264, in <module>
 239     model.learn(total_timesteps=total_timesteps, progress_bar=True, callback=callback_list)
 240   File "/home/src/crossq/sbx/sac/sac.py", line 187, in learn
 241     return super().learn(
 242            ^^^^^^^^^^^^^^
 243   File "/home/miniconda3/envs/crossq/lib/python3.11/site-packages/stable_baselines3/common/off_policy_algorithm.py", line 312, in learn
 244     rollout = self.collect_rollouts(
 245               ^^^^^^^^^^^^^^^^^^^^^^
 246   File "/home/miniconda3/envs/crossq/lib/python3.11/site-packages/stable_baselines3/common/off_policy_algorithm.py", line 541, in collect_rollouts
 247     actions, buffer_actions = self._sample_action(learning_starts, action_noise, env.num_envs)
 248                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 249   File "/home/miniconda3/envs/crossq/lib/python3.11/site-packages/stable_baselines3/common/off_policy_algorithm.py", line 373, in _sample_action
 250     unscaled_action, _ = self.predict(self._last_obs, deterministic=False)
 251                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 252   File "/home/miniconda3/envs/crossq/lib/python3.11/site-packages/stable_baselines3/common/base_class.py", line 555, in predict
 253     return self.policy.predict(observation, state, episode_start, deterministic)
 254            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 255   File "/home/src/crossq/sbx/common/policies.py", line 64, in predict
 256     actions = self._predict(observation, deterministic=deterministic)
 257               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 258   File "/home/src/crossq/sbx/sac/policies.py", line 482, in _predict
 259     self.debug_log_action(observation, action, "_predict")
 260   File "/home/src/crossq/sbx/sac/policies.py", line 531, in debug_log_action
 261     raise ValueError("Action is None")
 262 ValueError: Action is None

When the error happens I added printing the state of the actor and the observation. nan values are mostly present in BatchRenorm:

Observations:  [[-0.98452299 -0.17525546 -0.13700339]]
Actor state:  ActorTrainState(step=Array(72082, dtype=int32, weak_type=True), apply_fn=<bound method Module.apply of Actor(
    # attributes
    net_arch = [256, 256]
    action_dim = 1
    batch_norm_momentum = 0.99
    log_std_min = -20
    log_std_max = 2
    use_batch_norm = True
    bn_mode = 'brn_actor'
)>, params={'BatchRenorm_0': {'bias': Array([nan, nan, nan], dtype=float32), 'scale': Array([nan, nan, nan], dtype=float32)}, 'BatchRenorm_1': {'bias': Array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
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       -5.07392526e-01, -1.65657967e-01, -3.15613002e-01, -1.74545765e-01,
       -2.78588176e-01, -4.34532404e-01, -2.61619866e-01, -1.43855408e-01,
                   nan,             nan, -3.72981817e-01, -1.94371045e-01,
        1.83636006e-02, -4.24602851e-02, -8.58307257e-02, -2.71321237e-01,
       -1.97004348e-01, -4.95876729e-01, -4.74496722e-01,             nan,
       -3.33254598e-02, -4.79034781e-01, -2.55109280e-01, -1.87851325e-01,
       -3.39175999e-01, -4.80552763e-01, -4.50025231e-01, -1.03966720e-01,
       -7.74463296e-01, -5.16545363e-02,  1.01213539e-02,             nan,
       -1.24744892e-01, -2.21584707e-01, -2.19108924e-01, -4.01318192e-01,
       -2.04100892e-01, -2.66580433e-01, -7.59028137e-01,             nan,
       -2.50042826e-01, -4.02819782e-01, -2.02461675e-01, -3.39741558e-01,
       -5.28345779e-02, -8.42932388e-02, -1.62568614e-01,  1.58098206e-01,
       -1.10761724e-01,  2.35181837e-03, -3.26542675e-01,             nan,
        5.05282357e-03, -1.25751108e-01,             nan, -3.03706586e-01,
                   nan, -2.41995305e-01, -2.53088415e-01, -2.43461326e-01,
       -2.04102136e-02, -1.84795737e-01, -2.18806162e-01,             nan,
       -2.36812025e-01, -1.80641860e-01, -3.41657400e-01, -3.14457595e-01,
       -2.63056546e-01, -3.97427410e-01, -2.54380584e-01,             nan,
                   nan, -1.74986079e-01, -2.74913579e-01, -1.29359856e-01,
       -3.59678119e-02,  3.35261613e-01, -8.78777653e-02, -5.04442751e-01,
       -4.00152236e-01, -2.42632881e-01,             nan, -4.05929804e-01,
       -2.45563030e-01, -1.88916773e-01, -2.40435839e-01, -1.00784957e-01,
                   nan,             nan, -3.35613132e-01, -1.17802337e-01,
                   nan,             nan, -2.27645561e-01,             nan,
       -3.03044826e-01,  5.99465857e-04, -1.56689212e-01, -1.43252518e-02,
       -1.03414640e-01, -3.61972488e-02, -2.86053956e-01,  1.54133691e-02,
       -2.91877747e-01, -4.44084078e-01, -1.14257067e-01, -3.59545112e-01,
       -1.86518461e-01, -4.90693688e-01, -1.78244472e-01, -4.35604304e-01,
       -1.25659660e-01, -1.01315916e-01, -1.45916626e-01, -2.43625432e-01,
       -1.30847663e-01,             nan, -1.70976147e-01, -1.98871285e-01,
                   nan,  3.85484435e-02, -3.26892465e-01, -2.91502178e-01,
                   nan, -1.78116813e-01, -1.97384760e-01, -2.32053742e-01,
       -2.82236040e-01, -1.08087726e-01, -4.09883052e-01, -5.29915988e-01,
       -3.24332803e-01, -1.00257874e-01,             nan,             nan,
                   nan, -2.54310161e-01,             nan, -5.29110789e-01,
       -2.61053085e-01, -5.08699298e-01, -2.21153900e-01, -5.59086382e-01,
       -2.46261105e-01,             nan,             nan, -3.05840541e-02,
       -2.34860018e-01, -3.22149009e-01, -3.99790168e-01, -2.31906787e-01,
       -3.94329689e-02,  8.35715458e-02, -2.45865479e-01, -4.13744181e-01,
                   nan, -4.18445647e-01,             nan, -3.28062594e-01,
                   nan, -4.72936690e-01, -1.81261748e-01,  1.46970540e-01],      dtype=float32), 'kernel': Array([[ 4.8442278e-02, -2.5370871e-04,  1.9979328e-01, ...,
         1.5068804e-01,  3.6767788e-02,  1.3192339e-01],
       [-6.7832903e-03, -5.0304595e-02, -2.1431591e-01, ...,
        -1.1181718e-01,  2.0614813e-01,  1.2734850e-01],
       [ 1.3255176e-01,  5.4206248e-02,  1.9638033e-01, ...,
        -5.9157098e-03, -1.5652535e-02, -5.7662982e-03],
       ...,
       [ 2.7093706e-03, -4.8780489e-01, -1.7505699e-01, ...,
         7.1161285e-02,  2.8860131e-02,  5.7024822e-02],
       [ 8.8524841e-02, -6.1251257e-02, -2.8650817e-02, ...,
        -9.4492398e-02,  2.4803801e-01,  7.7640779e-02],
       [-1.5573186e-01, -1.6367893e-01, -1.5592015e-01, ...,
        -1.1927266e-01, -2.0962511e-01, -9.1291368e-02]], dtype=float32)}, [skipped many lines]
        
        Action:  [[nan]]

The log is not complete as it has more than 100KB in size, so I attach just the beginning.
@JankowskiChristopher
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@adityab @danielpalen in order to reproduce this error I provide more information below.
Tested tasks and seeds that crashed:

  • pendulum-swingup, seeds from 0 to 9 (inclusive)
  • dog-stand, seed 0

Wandb charts for dog-stand seed 0 (training crashed after 400k steps):
image
image

Action values were nan and nan values were present mostly in BatchRenorm layers, but also in some dense layers - similar to the log above with pendulum-swingup.

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@JankowskiChristopher