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output_cppo_deepseek_risk_100_epochs_01.log
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nohup: ignoring input
[scw-cranky-chebyshev][[42265,1],0][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],0][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],3][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],7][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],6][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],0][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],7][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],6][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],6][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],4][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],7][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],3][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],7][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],7][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],0][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],3][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],3][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],7][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],0][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],4][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],6][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],2][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],6][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],4][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],2][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],6][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],4][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],6][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],4][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],7][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],3][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],0][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],3][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],0][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],2][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],3][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],2][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],4][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],2][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],4][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],2][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],2][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],5][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],5][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],5][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],5][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],5][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],5][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],5][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],1][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],1][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],1][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],1][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],1][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],1][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
[scw-cranky-chebyshev][[42265,1],1][btl_tcp_proc.c:266:mca_btl_tcp_proc_create_interface_graph] Unable to find reachable pairing between local and remote interfaces
/root/FinRL_LLM/train_cppo_llm_risk_01.py:60: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
train['llm_sentiment'].fillna(0, inplace=True) #0 is outside scope of sentiment scores (min is 1)
/root/FinRL_LLM/train_cppo_llm_risk_01.py:62: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
train['llm_risk'].fillna(3, inplace=True) #neutral risk score is 3
Stock Dimension: 84, State Space: 1009
Warning: Log dir /root/spinningup_pytorch/data/cppo/cppo_s0 already exists! Storing info there anyway.
[32;1mLogging data to /root/spinningup_pytorch/data/cppo/cppo_s0/progress.txt[0m
[36;1mSaving config:
[0m
{
"ac_kwargs": {
"hidden_sizes": [
512,
512
]
},
"actor_critic": "MLPActorCritic",
"alpha": 0.85,
"beta": 3000.0,
"clip_ratio": 0.7,
"cvar_clip_ratio": 0.05,
"delay": 1.0,
"env_fn": "<function <lambda> at 0x7dfb98032680>",
"epochs": 100,
"exp_name": "cppo",
"gamma": 0.995,
"lam": 0.95,
"lam_low_bound": 0.001,
"lam_lr": 0.0005,
"lam_start": 0.01,
"logger": {
"<spinup.utils.logx.EpochLogger object at 0x7dfb980c80d0>": {
"epoch_dict": {},
"exp_name": "cppo",
"first_row": true,
"log_current_row": {},
"log_headers": [],
"output_dir": "/root/spinningup_pytorch/data/cppo/cppo_s0",
"output_file": {
"<_io.TextIOWrapper name='/root/spinningup_pytorch/data/cppo/cppo_s0/progress.txt' mode='w' encoding='UTF-8'>": {
"mode": "w"
}
}
}
},
"logger_kwargs": {
"exp_name": "cppo",
"output_dir": "/root/spinningup_pytorch/data/cppo/cppo_s0"
},
"max_ep_len": 3000,
"nu_delay": 0.75,
"nu_lr": 0.0005,
"nu_start": 0.1,
"pi_lr": 3e-05,
"save_freq": 10,
"seed": 0,
"steps_per_epoch": 20000,
"target_kl": 0.35,
"train_pi_iters": 100,
"train_v_iters": 100,
"vf_lr": 0.0001
}
/root/FinRL_LLM/train_cppo_llm_risk_01.py:60: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
train['llm_sentiment'].fillna(0, inplace=True) #0 is outside scope of sentiment scores (min is 1)
/root/FinRL_LLM/train_cppo_llm_risk_01.py:62: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
train['llm_risk'].fillna(3, inplace=True) #neutral risk score is 3
Stock Dimension: 84, State Space: 1009
/root/FinRL_LLM/train_cppo_llm_risk_01.py:60: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
train['llm_sentiment'].fillna(0, inplace=True) #0 is outside scope of sentiment scores (min is 1)
/root/FinRL_LLM/train_cppo_llm_risk_01.py:62: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
train['llm_risk'].fillna(3, inplace=True) #neutral risk score is 3
Stock Dimension: 84, State Space: 1009
/root/FinRL_LLM/train_cppo_llm_risk_01.py:60: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
train['llm_sentiment'].fillna(0, inplace=True) #0 is outside scope of sentiment scores (min is 1)
/root/FinRL_LLM/train_cppo_llm_risk_01.py:62: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
train['llm_risk'].fillna(3, inplace=True) #neutral risk score is 3
Stock Dimension: 84, State Space: 1009
/root/FinRL_LLM/train_cppo_llm_risk_01.py:60: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
train['llm_sentiment'].fillna(0, inplace=True) #0 is outside scope of sentiment scores (min is 1)
/root/FinRL_LLM/train_cppo_llm_risk_01.py:62: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
train['llm_risk'].fillna(3, inplace=True) #neutral risk score is 3
Stock Dimension: 84, State Space: 1009
/root/FinRL_LLM/train_cppo_llm_risk_01.py:60: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
train['llm_sentiment'].fillna(0, inplace=True) #0 is outside scope of sentiment scores (min is 1)
/root/FinRL_LLM/train_cppo_llm_risk_01.py:62: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
train['llm_risk'].fillna(3, inplace=True) #neutral risk score is 3
Stock Dimension: 84, State Space: 1009
/root/FinRL_LLM/train_cppo_llm_risk_01.py:60: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
train['llm_sentiment'].fillna(0, inplace=True) #0 is outside scope of sentiment scores (min is 1)
/root/FinRL_LLM/train_cppo_llm_risk_01.py:62: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
train['llm_risk'].fillna(3, inplace=True) #neutral risk score is 3
Stock Dimension: 84, State Space: 1009
/root/FinRL_LLM/train_cppo_llm_risk_01.py:60: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
train['llm_sentiment'].fillna(0, inplace=True) #0 is outside scope of sentiment scores (min is 1)
/root/FinRL_LLM/train_cppo_llm_risk_01.py:62: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
train['llm_risk'].fillna(3, inplace=True) #neutral risk score is 3
Stock Dimension: 84, State Space: 1009
[32;1m
Number of parameters: pi: 822952, v: 780289
[0m
Warning: trajectory cut off by epoch at 991 steps.
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Warning: trajectory cut off by epoch at 991 steps.
-------------------------------------
bad_trajectory_num: 0
update num: 0
-------------------------------------
bad_trajectory_num: 0
update num: 0
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bad_trajectory_num: 0
update num: 0
---------------------------------------
| Epoch | 0 |
| AverageEpRet | 187 |
| StdEpRet | 38.4 |
| MaxEpRet | 273 |
| MinEpRet | 125 |
| EpLen | 1.51e+03 |
| AverageVVals | -0.161 |
| StdVVals | 0.187 |
| MaxVVals | 0.399 |
| MinVVals | -0.845 |
| TotalEnvInteracts | 2e+04 |
| LossPi | 1.83e-06 |
| LossV | 915 |
| DeltaLossPi | -0.443 |
| DeltaLossV | -433 |
| Entropy | 0.919 |
| KL | 0.254 |
| ClipFrac | 0.525 |
| StopIter | 99 |
| Time | 1.16e+03 |
---------------------------------------
-------------------------------------
bad_trajectory_num: 1
update num: 1
-------------------------------------
bad_trajectory_num: 0
update num: 0
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bad_trajectory_num: 1
update num: 1
nu: [67.72072]
lam: 1.5099500000000001
-------------------------------------
nu: [72.49433]
lam: 1.5099500000000001
-------------------------------------
nu: [71.3462]
lam: 1.5099500000000001
-------------------------------------
nu: [69.73793]
lam: 1.5099500000000001
-------------------------------------
nu: [63.49749]
lam: 1.5099500000000001
-------------------------------------
nu: [99.789]
lam: 1.5099500000000001
-------------------------------------
-------------------------------------
bad_trajectory_num: 0
update num: 0
nu: [77.58859]
lam: 1.5099500000000001
-------------------------------------
-------------------------------------
bad_trajectory_num: 0
update num: 0
nu: [73.70684]
lam: 1.5099500000000001
-------------------------------------
Warning: trajectory cut off by epoch at 991 steps.
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bad_trajectory_num: 814
update num: 814
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bad_trajectory_num: 809
update num: 808
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bad_trajectory_num: 753
update num: 753
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bad_trajectory_num: 741
update num: 739
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bad_trajectory_num: 686
update num: 685
---------------------------------------
| Epoch | 1 |
| AverageEpRet | 181 |
| StdEpRet | 34.8 |
| MaxEpRet | 245 |
| MinEpRet | 142 |
| EpLen | 1.51e+03 |
| AverageVVals | 15.1 |
| StdVVals | 1.04 |
| MaxVVals | 16.1 |
| MinVVals | 10.7 |
| TotalEnvInteracts | 4e+04 |
| LossPi | 3.41e-05 |
| LossV | 435 |
| DeltaLossPi | -0.449 |
| DeltaLossV | -127 |
| Entropy | 0.918 |
| KL | 0.264 |
| ClipFrac | 0.569 |
| StopIter | 99 |
| Time | 2.5e+03 |
---------------------------------------
-------------------------------------
bad_trajectory_num: 643
update num: 643
nu: [88.91398]
nu: [82.31406]
lam: [2.9711556]
-------------------------------------
nu: [80.036476]
lam: [2.9760897]
-------------------------------------
nu: [74.35384]
lam: [2.974277]
-------------------------------------
nu: [78.03257]
lam: [2.9782014]
-------------------------------------
-------------------------------------
bad_trajectory_num: 1432
update num: 1431
nu: [70.34181]
lam: [2.9600556]
-------------------------------------
nu: [81.72661]
lam: [2.975081]
-------------------------------------
lam: [2.973703]
-------------------------------------
-------------------------------------
bad_trajectory_num: 818
update num: 817
nu: [75.71634]
lam: [2.9730968]
-------------------------------------
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bad_trajectory_num: 716
update num: 716
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bad_trajectory_num: 880
update num: 878
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bad_trajectory_num: 824
update num: 824
-------------------------------------
bad_trajectory_num: 740
update num: 740
nu: [83.8541]
nu: [80.584145]
lam: [4.435239]
-------------------------------------
---------------------------------------
| Epoch | 2 |
| AverageEpRet | 170 |
| StdEpRet | 30.2 |
| MaxEpRet | 215 |
| MinEpRet | 133 |
| EpLen | 1.51e+03 |
| AverageVVals | 16.7 |
| StdVVals | 4.42 |
| MaxVVals | 19.9 |
| MinVVals | -7.03 |
| TotalEnvInteracts | 6e+04 |
| LossPi | 1.74e-05 |
| LossV | 351 |
| DeltaLossPi | -0.462 |
| DeltaLossV | -226 |
| Entropy | 0.917 |
| KL | 0.284 |
| ClipFrac | 0.59 |
| StopIter | 99 |
| Time | 3.83e+03 |
---------------------------------------
-------------------------------------
bad_trajectory_num: 1113
update num: 1113
nu: [69.95619]
lam: [4.429246]
-------------------------------------
-------------------------------------
bad_trajectory_num: 980
update num: 980
nu: [70.54405]
-------------------------------------
bad_trajectory_num: 752
update num: 752
nu: [92.18471]
lam: [4.4360714]
-------------------------------------
nu: [82.869446]
lam: [4.434218]
-------------------------------------
nu: [80.282425]
lam: [4.4371004]
-------------------------------------
lam: [4.429999]
-------------------------------------
lam: [4.439185]
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bad_trajectory_num: 629
update num: 629
nu: [88.69591]
lam: [4.424885]
-------------------------------------
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bad_trajectory_num: 1025
update num: 1022
---------------------------------------
| Epoch | 3 |
| AverageEpRet | 154 |
| StdEpRet | 26.2 |
| MaxEpRet | 191 |
| MinEpRet | 110 |
| EpLen | 1.51e+03 |
| AverageVVals | 14.4 |
| StdVVals | 10.9 |
| MaxVVals | 24.5 |
| MinVVals | -19 |
| TotalEnvInteracts | 8e+04 |
| LossPi | 1.18e-05 |
| LossV | 237 |
| DeltaLossPi | -0.462 |
| DeltaLossV | -170 |
| Entropy | 0.915 |
| KL | 0.328 |
| ClipFrac | 0.574 |
-------------------------------------
bad_trajectory_num: 925
update num: 925
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bad_trajectory_num: 537
update num: 537
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bad_trajectory_num: 790
update num: 790
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bad_trajectory_num: 1065
update num: 1065
nu: [69.92035]
lam: [5.889979]
-------------------------------------
nu: [69.700096]
lam: [5.894947]
-------------------------------------
-------------------------------------
bad_trajectory_num: 940
update num: 938
nu: [66.21593]
lam: [5.892783]
-------------------------------------
| StopIter | 99 |
| Time | 4.87e+03 |
---------------------------------------
-------------------------------------
bad_trajectory_num: 1421
update num: 1421
nu: [68.98251]
lam: [5.880537]
-------------------------------------
nu: [73.59071]
lam: [5.8969593]
-------------------------------------
nu: [89.50329]
lam: [5.894268]
-------------------------------------
nu: [71.052536]
lam: [5.9039135]
-------------------------------------
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bad_trajectory_num: 937
update num: 937
nu: [78.507126]
lam: [5.888072]
-------------------------------------
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bad_trajectory_num: 628
update num: 628
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| Epoch | 4 |
| AverageEpRet | 201 |
| StdEpRet | 18.3 |
| MaxEpRet | 222 |
| MinEpRet | 167 |
| EpLen | 1.51e+03 |
| AverageVVals | 17.3 |
| StdVVals | 11.9 |
| MaxVVals | 27.5 |
| MinVVals | -23.1 |
| TotalEnvInteracts | 1e+05 |
| LossPi | 3.28e-05 |
| LossV | 311 |
| DeltaLossPi | -0.455 |
| DeltaLossV | -221 |
| Entropy | 0.915 |
| KL | 0.331 |
| ClipFrac | 0.582 |
| StopIter | 99 |
| Time | 5.78e+03 |
---------------------------------------
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bad_trajectory_num: 529
update num: 529
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bad_trajectory_num: 636
update num: 635
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bad_trajectory_num: 574
update num: 572
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bad_trajectory_num: 516
update num: 515
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bad_trajectory_num: 739
update num: 739
nu: [91.04035]
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bad_trajectory_num: 402
update num: 402
lam: [7.348819]
-------------------------------------
nu: [83.86003]
nu: [84.36322]
nu: [91.391785]
nu: [88.53801]
nu: [93.210045]
lam: [7.3495164]
-------------------------------------
nu: [90.29008]
lam: [7.3596754]
-------------------------------------
lam: [7.346046]
-------------------------------------
lam: [7.3601637]
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lam: [7.368387]
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lam: [7.3550186]
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bad_trajectory_num: 510
update num: 509
nu: [81.71245]
lam: [7.360097]
-------------------------------------
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bad_trajectory_num: 1065
update num: 1065
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bad_trajectory_num: 667
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bad_trajectory_num: 662
update num: 662
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bad_trajectory_num: 1126
update num: 1125
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bad_trajectory_num: 1076
update num: 1075
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bad_trajectory_num: 1280
update num: 1279
---------------------------------------
| Epoch | 5 |
| AverageEpRet | 174 |
| StdEpRet | 46.4 |
| MaxEpRet | 251 |
| MinEpRet | 88.6 |
| EpLen | 1.51e+03 |
| AverageVVals | 18.5 |
| StdVVals | 15.6 |
| MaxVVals | 31.6 |
| MinVVals | -29.5 |
| TotalEnvInteracts | 1.2e+05 |
| LossPi | -8.99e-06 |
| LossV | 254 |
| DeltaLossPi | -0.469 |
| DeltaLossV | -193 |
| Entropy | 0.913 |
| KL | 0.354 |
nu: [81.666534]
| ClipFrac | 0.599 |
| StopIter | 99 |
| Time | 6.71e+03 |
---------------------------------------
lam: [8.803299]
-------------------------------------
-------------------------------------
nu: [72.14554]
bad_trajectory_num: 710
update num: 710
nu: [66.00758]
lam: [8.81075]
-------------------------------------
nu: [78.59572]
lam: [8.81453]
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nu: [95.456825]
lam: [8.826457]
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nu: [96.56221]
lam: [8.814467]
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lam: [8.802912]
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bad_trajectory_num: 627
update num: 627
nu: [88.884254]
lam: [8.819241]
-------------------------------------
nu: [86.9318]
lam: [8.8038645]
-------------------------------------
day: 1508, episode: 20
begin_total_asset: 1000000.00
end_total_asset: 2980250.06
total_reward: 1980250.06
total_cost: 53210.05
total_trades: 77832
Sharpe: 1.135
=================================
day: 1508, episode: 20
begin_total_asset: 1000000.00
end_total_asset: 4243974.97
total_reward: 3243974.97
total_cost: 89029.08
total_trades: 84662
Sharpe: 1.216
=================================
day: 1508, episode: 20
begin_total_asset: 1000000.00
end_total_asset: 3119875.80
total_reward: 2119875.80
total_cost: 48401.28
total_trades: 76397
Sharpe: 1.135
=================================
day: 1508, episode: 20
begin_total_asset: 1000000.00
end_total_asset: 2937685.54
total_reward: 1937685.54
total_cost: 69512.35
total_trades: 78903
Sharpe: 1.138
=================================
day: 1508, episode: 20
begin_total_asset: 1000000.00
end_total_asset: 2675526.88
total_reward: 1675526.88
total_cost: 39180.94
total_trades: 70883
Sharpe: 0.911
=================================
day: 1508, episode: 20
begin_total_asset: 1000000.00
end_total_asset: 3106355.90
total_reward: 2106355.90
total_cost: 45370.12
total_trades: 76885
Sharpe: 1.090
=================================
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day: 1508, episode: 20
begin_total_asset: 1000000.00
end_total_asset: 2169557.01
total_reward: 1169557.01
total_cost: 67785.21
total_trades: 77679
Sharpe: 0.849
=================================
day: 1508, episode: 20
begin_total_asset: 1000000.00
end_total_asset: 3009236.88
total_reward: 2009236.88
total_cost: 76572.96
total_trades: 80266
Sharpe: 1.069
=================================
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bad_trajectory_num: 880
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bad_trajectory_num: 539
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bad_trajectory_num: 1069
update num: 1069
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bad_trajectory_num: 600
update num: 600
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bad_trajectory_num: 568
update num: 567
---------------------------------------
| Epoch | 6 |
| AverageEpRet | 203 |
| StdEpRet | 54.2 |
nu: [102.96414]
| MaxEpRet | 324 |
| MinEpRet | 116 |
| EpLen | 1.51e+03 |
| AverageVVals | 18.4 |
| StdVVals | 15.3 |
| MaxVVals | 33.9 |
nu: [71.91608]
lam: [10.274798]
-------------------------------------
-------------------------------------
bad_trajectory_num: 1012
update num: 1011
nu: [84.3689]
lam: [10.266187]
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bad_trajectory_num: 308
update num: 308
nu: [100.69311]
lam: [10.277746]
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lam: [10.2624655]
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nu: [85.81393]
lam: [10.2787285]
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nu: [102.39338]
lam: [10.275232]
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nu: [92.08304]
lam: [10.266839]
-------------------------------------
| MinVVals | -30.4 |
| TotalEnvInteracts | 1.4e+05 |
| LossPi | 3.44e-05 |
| LossV | 317 |
| DeltaLossPi | -0.444 |
| DeltaLossV | -207 |
| Entropy | 0.912 |
| KL | 0.402 |
| ClipFrac | 0.579 |
| StopIter | 99 |
| Time | 7.61e+03 |
---------------------------------------
-------------------------------------
bad_trajectory_num: 687
update num: 686
nu: [86.65408]
lam: [10.260399]
-------------------------------------
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bad_trajectory_num: 940
update num: 940
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bad_trajectory_num: 793
update num: 793
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bad_trajectory_num: 923
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bad_trajectory_num: 1427
update num: 1426
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bad_trajectory_num: 1267
update num: 1267
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bad_trajectory_num: 650
update num: 650
nu: [94.3813]
lam: [11.710983]
---------------------------------------
| Epoch | 7 |
| AverageEpRet | 189 |
| StdEpRet | 17.6 |
| MaxEpRet | 214 |
| MinEpRet | 156 |
| EpLen | 1.51e+03 |
| AverageVVals | 18.7 |
| StdVVals | 19.8 |
| MaxVVals | 37.3 |
| MinVVals | -35.9 |
| TotalEnvInteracts | 1.6e+05 |
| LossPi | 4.43e-05 |
| LossV | 206 |
| DeltaLossPi | -0.48 |
-------------------------------------
bad_trajectory_num: 606
update num: 606
nu: [84.304214]
-------------------------------------
| DeltaLossV | -163 |
| Entropy | 0.911 |
| KL | 0.478 |
| ClipFrac | 0.616 |
| StopIter | 99 |
lam: [11.73884]
-------------------------------------
nu: [79.61939]
lam: [11.7274]
-------------------------------------
| Time | 8.55e+03 |
---------------------------------------
nu: [86.34064]
-------------------------------------
bad_trajectory_num: 882
update num: 879
nu: [83.069305]
lam: [11.724003]
-------------------------------------
nu: [89.858925]
nu: [80.84891]
lam: [11.724035]
-------------------------------------
nu: [78.59564]
lam: [11.735822]
-------------------------------------
lam: [11.720798]
-------------------------------------
lam: [11.717072]
-------------------------------------
Warning: trajectory cut off by epoch at 991 steps.
Warning: trajectory cut off by epoch at 991 steps.
Warning: trajectory cut off by epoch at 991 steps.
Warning: trajectory cut off by epoch at 991 steps.
Warning: trajectory cut off by epoch at 991 steps.
Warning: trajectory cut off by epoch at 991 steps.
Warning: trajectory cut off by epoch at 991 steps.
Warning: trajectory cut off by epoch at 991 steps.
-------------------------------------
bad_trajectory_num: 625
update num: 624
-------------------------------------
bad_trajectory_num: 493
update num: 492
-------------------------------------
bad_trajectory_num: 751
update num: 751
nu: [104.87511]
-------------------------------------
bad_trajectory_num: 654
update num: 652
nu: [92.542816]
lam: [13.196688]
-------------------------------------
-------------------------------------
bad_trajectory_num: 647
update num: 647
nu: [84.80213]
lam: [13.182501]
-------------------------------------
---------------------------------------
| Epoch | 8 |
| AverageEpRet | 193 |
| StdEpRet | 48.8 |
| MaxEpRet | 270 |
| MinEpRet | 134 |
| EpLen | 1.51e+03 |
| AverageVVals | 21.5 |
| StdVVals | 15.3 |
| MaxVVals | 39.5 |
| MinVVals | -35 |
| TotalEnvInteracts | 1.8e+05 |
| LossPi | 1.07e-05 |
| LossV | 179 |
| DeltaLossPi | -0.468 |
| DeltaLossV | -133 |
| Entropy | 0.91 |
| KL | 0.383 |
| ClipFrac | 0.601 |
| StopIter | 99 |
| Time | 9.44e+03 |
---------------------------------------
-------------------------------------
bad_trajectory_num: 731
update num: 730
nu: [80.41577]
lam: [13.177773]
-------------------------------------
-------------------------------------
bad_trajectory_num: 644
update num: 644
nu: [94.58453]
lam: [13.190892]
-------------------------------------
-------------------------------------
bad_trajectory_num: 908
update num: 908
nu: [74.904045]
lam: [13.187591]
-------------------------------------
nu: [119.088295]
lam: [13.180833]
-------------------------------------
nu: [85.78966]
lam: [13.180373]