Value error after running 50000 timesteps
arun-dezerv opened this issue · 1 comments
Stock Dimension: 30, State Space: 2371
<class 'stable_baselines3.common.vec_env.dummy_vec_env.DummyVecEnv'>
{'batch_size': 64, 'buffer_size': 100000, 'learning_rate': 0.001, 'learning_starts': 100, 'ent_coef': 'auto_0.1'}
Using cpu device
Logging to /content/drive/MyDrive/trained_model_bpm/sac_2010-01-01_2017-01-01_0.001_100000_1
| time/ | |
| episodes | 4 |
| fps | 20 |
| time_elapsed | 338 |
| total_timesteps | 7048 |
| train/ | |
| actor_loss | 2.31e+05 |
| critic_loss | 3.53e+06 |
| ent_coef | 84.8 |
| ent_coef_loss | -1.75e+03 |
| learning_rate | 0.001 |
| n_updates | 6947 |
| reward | 4.956748 |
| time/ | |
| episodes | 8 |
| fps | 20 |
| time_elapsed | 687 |
| total_timesteps | 14096 |
| train/ | |
| actor_loss | 2.58e+08 |
| critic_loss | 2.72e+13 |
| ent_coef | 9.74e+04 |
| ent_coef_loss | -4.53e+03 |
| learning_rate | 0.001 |
| n_updates | 13995 |
| reward | 4.956748 |
day: 1761, episode: 10
begin_total_asset: 1000000.00
end_total_asset: 3142988.68
total_reward: 2142988.68
total_cost: 0.00
total_trades: 29925
Sharpe: 1.135
| time/ | |
| episodes | 12 |
| fps | 20 |
| time_elapsed | 1035 |
| total_timesteps | 21144 |
| train/ | |
| actor_loss | 2.78e+11 |
| critic_loss | 2.76e+20 |
| ent_coef | 1.12e+08 |
| ent_coef_loss | -7.31e+03 |
| learning_rate | 0.001 |
| n_updates | 21043 |
| reward | 4.956748 |
| time/ | |
| episodes | 16 |
| fps | 20 |
| time_elapsed | 1388 |
| total_timesteps | 28192 |
| train/ | |
| actor_loss | 5.54e+13 |
| critic_loss | 2.92e+27 |
| ent_coef | 1.28e+11 |
| ent_coef_loss | -1.01e+04 |
| learning_rate | 0.001 |
| n_updates | 28091 |
| reward | 4.956748 |
day: 1761, episode: 20
begin_total_asset: 1000000.00
end_total_asset: 3142988.68
total_reward: 2142988.68
total_cost: 0.00
total_trades: 29925
Sharpe: 1.135
| time/ | |
| episodes | 20 |
| fps | 20 |
| time_elapsed | 1744 |
| total_timesteps | 35240 |
| train/ | |
| actor_loss | 6.23e+16 |
| critic_loss | 3.83e+33 |
| ent_coef | 1.47e+14 |
| ent_coef_loss | -1.28e+04 |
| learning_rate | 0.001 |
| n_updates | 35139 |
| reward | 4.956748 |
| time/ | |
| episodes | 24 |
| fps | 20 |
| time_elapsed | 2109 |
| total_timesteps | 42288 |
| train/ | |
| actor_loss | 7.17e+19 |
| critic_loss | inf |
| ent_coef | 1.69e+17 |
| ent_coef_loss | -1.57e+04 |
| learning_rate | 0.001 |
| n_updates | 42187 |
| reward | 4.956748 |
| time/ | |
| episodes | 28 |
| fps | 19 |
| time_elapsed | 2473 |
| total_timesteps | 49336 |
| train/ | |
| actor_loss | 8.21e+22 |
| critic_loss | inf |
| ent_coef | 1.93e+20 |
| ent_coef_loss | -1.84e+04 |
| learning_rate | 0.001 |
| n_updates | 49235 |
| reward | 4.956748 |
day: 1761, episode: 30
begin_total_asset: 1000000.00
end_total_asset: 3142988.68
total_reward: 2142988.68
total_cost: 0.00
total_trades: 29925
Sharpe: 1.135
ValueError Traceback (most recent call last)
in <cell line: 78>()
74 model_sac.set_logger(new_logger_sac)
75
---> 76 trained_sac = agent.train_model(model=model_sac,
77 tb_log_name='sac',
78 total_timesteps=timesteps) if if_using_sac else None
15 frames
/usr/local/lib/python3.10/dist-packages/torch/distributions/distribution.py in init(self, batch_shape, event_shape, validate_args)
66 valid = constraint.check(value)
67 if not valid.all():
---> 68 raise ValueError(
69 f"Expected parameter {param} "
70 f"({type(value).name} of shape {tuple(value.shape)}) "
ValueError: Expected parameter loc (Tensor of shape (1, 30)) of distribution Normal(loc: torch.Size([1, 30]), scale: torch.Size([1, 30])) to satisfy the constraint Real(), but found invalid values:
tensor([[nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
nan, nan, nan, nan, nan, nan]])
I am getting the above Value Error after the model has run for 50,000 timesteps. As you can see, the actor_loss and critic_loss hit very high values. Could this be causing this error? Also, total_trades is stuck at 29925 and not changing through multiple episodes. Any idea why this could be happening?
Thank you for bringing up the issue. Currently, the FinRL library is extremely poorly maintained. Rest assured, I will reorganize a team to ensure its proper maintenance.
Best regards,
Bruce Yang