Commit ·
247e099
1
Parent(s): 09ef5eb
Upload folder using huggingface_hub
Browse files- .gitattributes +1 -0
- .summary/0/events.out.tfevents.1694695704.rhmmedcatt-ProLiant-ML350-Gen10 +3 -0
- .summary/1/events.out.tfevents.1694695704.rhmmedcatt-ProLiant-ML350-Gen10 +3 -0
- README.md +56 -0
- checkpoint_p0/best_000002950_12083200_reward_7.781.pth +3 -0
- checkpoint_p0/checkpoint_000002837_11620352.pth +3 -0
- checkpoint_p0/checkpoint_000002950_12083200.pth +3 -0
- checkpoint_p1/best_000002353_9637888_reward_8.260.pth +3 -0
- checkpoint_p1/checkpoint_000002372_9715712.pth +3 -0
- checkpoint_p1/checkpoint_000002442_10002432.pth +3 -0
- config.json +142 -0
- git.diff +0 -0
- replay.mp4 +3 -0
- sf_log.txt +947 -0
.gitattributes
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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replay.mp4 filter=lfs diff=lfs merge=lfs -text
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.summary/0/events.out.tfevents.1694695704.rhmmedcatt-ProLiant-ML350-Gen10
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version https://git-lfs.github.com/spec/v1
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oid sha256:16269aa4c71f6002c20f65c35fd2a052055180f25988e26e057cb148f87fc787
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.summary/1/events.out.tfevents.1694695704.rhmmedcatt-ProLiant-ML350-Gen10
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version https://git-lfs.github.com/spec/v1
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README.md
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---
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library_name: sample-factory
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tags:
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- deep-reinforcement-learning
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- reinforcement-learning
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- sample-factory
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model-index:
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- name: APPO
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results:
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- task:
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type: reinforcement-learning
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name: reinforcement-learning
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dataset:
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name: doom_battle2
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type: doom_battle2
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metrics:
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- type: mean_reward
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value: 6.50 +/- 3.01
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name: mean_reward
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verified: false
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---
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A(n) **APPO** model trained on the **doom_battle2** environment.
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This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
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Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
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## Downloading the model
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After installing Sample-Factory, download the model with:
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```
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python -m sample_factory.huggingface.load_from_hub -r MattStammers/vizdoom_battle_two
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```
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## Using the model
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To run the model after download, use the `enjoy` script corresponding to this environment:
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```
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python -m <path.to.enjoy.module> --algo=APPO --env=doom_battle2 --train_dir=./train_dir --experiment=vizdoom_battle_two
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```
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You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
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See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
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## Training with this model
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To continue training with this model, use the `train` script corresponding to this environment:
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```
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python -m <path.to.train.module> --algo=APPO --env=doom_battle2 --train_dir=./train_dir --experiment=vizdoom_battle_two --restart_behavior=resume --train_for_env_steps=10000000000
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```
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Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
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checkpoint_p0/best_000002950_12083200_reward_7.781.pth
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version https://git-lfs.github.com/spec/v1
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size 37627795
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checkpoint_p0/checkpoint_000002837_11620352.pth
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version https://git-lfs.github.com/spec/v1
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checkpoint_p0/checkpoint_000002950_12083200.pth
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version https://git-lfs.github.com/spec/v1
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size 37628387
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checkpoint_p1/best_000002353_9637888_reward_8.260.pth
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version https://git-lfs.github.com/spec/v1
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checkpoint_p1/checkpoint_000002372_9715712.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:0edcc2bc2a8682ee9c3af1713baa39869f99e0cff2278a399f28dd87fa206294
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size 37628387
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checkpoint_p1/checkpoint_000002442_10002432.pth
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version https://git-lfs.github.com/spec/v1
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size 37628387
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config.json
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{
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| 2 |
+
"help": false,
|
| 3 |
+
"algo": "APPO",
|
| 4 |
+
"env": "doom_battle2",
|
| 5 |
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"experiment": "default_experiment",
|
| 6 |
+
"train_dir": "/home/cogstack/Documents/optuna/environments/sample_factory/train_dir",
|
| 7 |
+
"restart_behavior": "restart",
|
| 8 |
+
"device": "gpu",
|
| 9 |
+
"seed": null,
|
| 10 |
+
"num_policies": 2,
|
| 11 |
+
"async_rl": true,
|
| 12 |
+
"serial_mode": false,
|
| 13 |
+
"batched_sampling": false,
|
| 14 |
+
"num_batches_to_accumulate": 2,
|
| 15 |
+
"worker_num_splits": 2,
|
| 16 |
+
"policy_workers_per_policy": 1,
|
| 17 |
+
"max_policy_lag": 1000,
|
| 18 |
+
"num_workers": 8,
|
| 19 |
+
"num_envs_per_worker": 4,
|
| 20 |
+
"batch_size": 1024,
|
| 21 |
+
"num_batches_per_epoch": 1,
|
| 22 |
+
"num_epochs": 1,
|
| 23 |
+
"rollout": 32,
|
| 24 |
+
"recurrence": 32,
|
| 25 |
+
"shuffle_minibatches": false,
|
| 26 |
+
"gamma": 0.99,
|
| 27 |
+
"reward_scale": 1.0,
|
| 28 |
+
"reward_clip": 1000.0,
|
| 29 |
+
"value_bootstrap": false,
|
| 30 |
+
"normalize_returns": true,
|
| 31 |
+
"exploration_loss_coeff": 0.001,
|
| 32 |
+
"value_loss_coeff": 0.5,
|
| 33 |
+
"kl_loss_coeff": 0.0,
|
| 34 |
+
"exploration_loss": "symmetric_kl",
|
| 35 |
+
"gae_lambda": 0.95,
|
| 36 |
+
"ppo_clip_ratio": 0.1,
|
| 37 |
+
"ppo_clip_value": 0.2,
|
| 38 |
+
"with_vtrace": false,
|
| 39 |
+
"vtrace_rho": 1.0,
|
| 40 |
+
"vtrace_c": 1.0,
|
| 41 |
+
"optimizer": "adam",
|
| 42 |
+
"adam_eps": 1e-06,
|
| 43 |
+
"adam_beta1": 0.9,
|
| 44 |
+
"adam_beta2": 0.999,
|
| 45 |
+
"max_grad_norm": 4.0,
|
| 46 |
+
"learning_rate": 0.0001,
|
| 47 |
+
"lr_schedule": "constant",
|
| 48 |
+
"lr_schedule_kl_threshold": 0.008,
|
| 49 |
+
"lr_adaptive_min": 1e-06,
|
| 50 |
+
"lr_adaptive_max": 0.01,
|
| 51 |
+
"obs_subtract_mean": 0.0,
|
| 52 |
+
"obs_scale": 255.0,
|
| 53 |
+
"normalize_input": true,
|
| 54 |
+
"normalize_input_keys": null,
|
| 55 |
+
"decorrelate_experience_max_seconds": 0,
|
| 56 |
+
"decorrelate_envs_on_one_worker": true,
|
| 57 |
+
"actor_worker_gpus": [],
|
| 58 |
+
"set_workers_cpu_affinity": true,
|
| 59 |
+
"force_envs_single_thread": false,
|
| 60 |
+
"default_niceness": 0,
|
| 61 |
+
"log_to_file": true,
|
| 62 |
+
"experiment_summaries_interval": 10,
|
| 63 |
+
"flush_summaries_interval": 30,
|
| 64 |
+
"stats_avg": 100,
|
| 65 |
+
"summaries_use_frameskip": true,
|
| 66 |
+
"heartbeat_interval": 20,
|
| 67 |
+
"heartbeat_reporting_interval": 600,
|
| 68 |
+
"train_for_env_steps": 10000000,
|
| 69 |
+
"train_for_seconds": 10000000000,
|
| 70 |
+
"save_every_sec": 120,
|
| 71 |
+
"keep_checkpoints": 2,
|
| 72 |
+
"load_checkpoint_kind": "latest",
|
| 73 |
+
"save_milestones_sec": -1,
|
| 74 |
+
"save_best_every_sec": 5,
|
| 75 |
+
"save_best_metric": "reward",
|
| 76 |
+
"save_best_after": 100000,
|
| 77 |
+
"benchmark": false,
|
| 78 |
+
"encoder_mlp_layers": [
|
| 79 |
+
512,
|
| 80 |
+
512
|
| 81 |
+
],
|
| 82 |
+
"encoder_conv_architecture": "convnet_simple",
|
| 83 |
+
"encoder_conv_mlp_layers": [
|
| 84 |
+
512
|
| 85 |
+
],
|
| 86 |
+
"use_rnn": true,
|
| 87 |
+
"rnn_size": 512,
|
| 88 |
+
"rnn_type": "gru",
|
| 89 |
+
"rnn_num_layers": 1,
|
| 90 |
+
"decoder_mlp_layers": [],
|
| 91 |
+
"nonlinearity": "elu",
|
| 92 |
+
"policy_initialization": "orthogonal",
|
| 93 |
+
"policy_init_gain": 1.0,
|
| 94 |
+
"actor_critic_share_weights": true,
|
| 95 |
+
"adaptive_stddev": true,
|
| 96 |
+
"continuous_tanh_scale": 0.0,
|
| 97 |
+
"initial_stddev": 1.0,
|
| 98 |
+
"use_env_info_cache": false,
|
| 99 |
+
"env_gpu_actions": false,
|
| 100 |
+
"env_gpu_observations": true,
|
| 101 |
+
"env_frameskip": 4,
|
| 102 |
+
"env_framestack": 1,
|
| 103 |
+
"pixel_format": "CHW",
|
| 104 |
+
"use_record_episode_statistics": false,
|
| 105 |
+
"with_wandb": false,
|
| 106 |
+
"wandb_user": null,
|
| 107 |
+
"wandb_project": "sample_factory",
|
| 108 |
+
"wandb_group": null,
|
| 109 |
+
"wandb_job_type": "SF",
|
| 110 |
+
"wandb_tags": [],
|
| 111 |
+
"with_pbt": false,
|
| 112 |
+
"pbt_mix_policies_in_one_env": true,
|
| 113 |
+
"pbt_period_env_steps": 5000000,
|
| 114 |
+
"pbt_start_mutation": 20000000,
|
| 115 |
+
"pbt_replace_fraction": 0.3,
|
| 116 |
+
"pbt_mutation_rate": 0.15,
|
| 117 |
+
"pbt_replace_reward_gap": 0.1,
|
| 118 |
+
"pbt_replace_reward_gap_absolute": 1e-06,
|
| 119 |
+
"pbt_optimize_gamma": false,
|
| 120 |
+
"pbt_target_objective": "true_objective",
|
| 121 |
+
"pbt_perturb_min": 1.1,
|
| 122 |
+
"pbt_perturb_max": 1.5,
|
| 123 |
+
"num_agents": -1,
|
| 124 |
+
"num_humans": 0,
|
| 125 |
+
"num_bots": -1,
|
| 126 |
+
"start_bot_difficulty": null,
|
| 127 |
+
"timelimit": null,
|
| 128 |
+
"res_w": 128,
|
| 129 |
+
"res_h": 72,
|
| 130 |
+
"wide_aspect_ratio": false,
|
| 131 |
+
"eval_env_frameskip": 1,
|
| 132 |
+
"fps": 35,
|
| 133 |
+
"command_line": "--env=doom_dm --num_workers=8 --num_envs_per_worker=4 --train_for_env_steps=20000000",
|
| 134 |
+
"cli_args": {
|
| 135 |
+
"env": "doom_dm",
|
| 136 |
+
"num_workers": 8,
|
| 137 |
+
"num_envs_per_worker": 4,
|
| 138 |
+
"train_for_env_steps": 20000000
|
| 139 |
+
},
|
| 140 |
+
"git_hash": "20b6d44612dad7d171f23e13b1f3b4c5e5631cf9",
|
| 141 |
+
"git_repo_name": "https://github.com/MattStammers/optuna.git"
|
| 142 |
+
}
|
git.diff
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
replay.mp4
ADDED
|
@@ -0,0 +1,3 @@
|
|
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|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f1ba30b31506c47d6ebdca647cc817565f595add0958c7fc6af44bb8c9aa1e2e
|
| 3 |
+
size 29487665
|
sf_log.txt
ADDED
|
@@ -0,0 +1,947 @@
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|
| 1 |
+
[2023-09-14 13:48:28,103][43239] Using GPUs [0] for process 0 (actually maps to GPUs [0])
|
| 2 |
+
[2023-09-14 13:48:28,103][43239] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for learning process 0
|
| 3 |
+
[2023-09-14 13:48:28,144][43239] Num visible devices: 1
|
| 4 |
+
[2023-09-14 13:48:28,195][43239] Starting seed is not provided
|
| 5 |
+
[2023-09-14 13:48:28,196][43239] Using GPUs [0] for process 0 (actually maps to GPUs [0])
|
| 6 |
+
[2023-09-14 13:48:28,196][43239] Initializing actor-critic model on device cuda:0
|
| 7 |
+
[2023-09-14 13:48:28,196][43239] RunningMeanStd input shape: (23,)
|
| 8 |
+
[2023-09-14 13:48:28,197][43239] RunningMeanStd input shape: (3, 72, 128)
|
| 9 |
+
[2023-09-14 13:48:28,197][43239] RunningMeanStd input shape: (1,)
|
| 10 |
+
[2023-09-14 13:48:28,209][43239] ConvEncoder: input_channels=3
|
| 11 |
+
[2023-09-14 13:48:28,370][43239] Conv encoder output size: 512
|
| 12 |
+
[2023-09-14 13:48:28,371][43239] Policy head output size: 640
|
| 13 |
+
[2023-09-14 13:48:28,389][43239] Created Actor Critic model with architecture:
|
| 14 |
+
[2023-09-14 13:48:28,389][43239] ActorCriticSharedWeights(
|
| 15 |
+
(obs_normalizer): ObservationNormalizer(
|
| 16 |
+
(running_mean_std): RunningMeanStdDictInPlace(
|
| 17 |
+
(running_mean_std): ModuleDict(
|
| 18 |
+
(measurements): RunningMeanStdInPlace()
|
| 19 |
+
(obs): RunningMeanStdInPlace()
|
| 20 |
+
)
|
| 21 |
+
)
|
| 22 |
+
)
|
| 23 |
+
(returns_normalizer): RecursiveScriptModule(original_name=RunningMeanStdInPlace)
|
| 24 |
+
(encoder): VizdoomEncoder(
|
| 25 |
+
(basic_encoder): ConvEncoder(
|
| 26 |
+
(enc): RecursiveScriptModule(
|
| 27 |
+
original_name=ConvEncoderImpl
|
| 28 |
+
(conv_head): RecursiveScriptModule(
|
| 29 |
+
original_name=Sequential
|
| 30 |
+
(0): RecursiveScriptModule(original_name=Conv2d)
|
| 31 |
+
(1): RecursiveScriptModule(original_name=ELU)
|
| 32 |
+
(2): RecursiveScriptModule(original_name=Conv2d)
|
| 33 |
+
(3): RecursiveScriptModule(original_name=ELU)
|
| 34 |
+
(4): RecursiveScriptModule(original_name=Conv2d)
|
| 35 |
+
(5): RecursiveScriptModule(original_name=ELU)
|
| 36 |
+
)
|
| 37 |
+
(mlp_layers): RecursiveScriptModule(
|
| 38 |
+
original_name=Sequential
|
| 39 |
+
(0): RecursiveScriptModule(original_name=Linear)
|
| 40 |
+
(1): RecursiveScriptModule(original_name=ELU)
|
| 41 |
+
)
|
| 42 |
+
)
|
| 43 |
+
)
|
| 44 |
+
(measurements_head): Sequential(
|
| 45 |
+
(0): Linear(in_features=23, out_features=128, bias=True)
|
| 46 |
+
(1): ELU(alpha=1.0)
|
| 47 |
+
(2): Linear(in_features=128, out_features=128, bias=True)
|
| 48 |
+
(3): ELU(alpha=1.0)
|
| 49 |
+
)
|
| 50 |
+
)
|
| 51 |
+
(core): ModelCoreRNN(
|
| 52 |
+
(core): GRU(640, 512)
|
| 53 |
+
)
|
| 54 |
+
(decoder): MlpDecoder(
|
| 55 |
+
(mlp): Identity()
|
| 56 |
+
)
|
| 57 |
+
(critic_linear): Linear(in_features=512, out_features=1, bias=True)
|
| 58 |
+
(action_parameterization): ActionParameterizationDefault(
|
| 59 |
+
(distribution_linear): Linear(in_features=512, out_features=21, bias=True)
|
| 60 |
+
)
|
| 61 |
+
)
|
| 62 |
+
[2023-09-14 13:48:29,272][43239] Using optimizer <class 'torch.optim.adam.Adam'>
|
| 63 |
+
[2023-09-14 13:48:29,273][43239] No checkpoints found
|
| 64 |
+
[2023-09-14 13:48:29,273][43239] Did not load from checkpoint, starting from scratch!
|
| 65 |
+
[2023-09-14 13:48:29,273][43239] Initialized policy 0 weights for model version 0
|
| 66 |
+
[2023-09-14 13:48:29,275][43239] LearnerWorker_p0 finished initialization!
|
| 67 |
+
[2023-09-14 13:48:29,275][43239] Using GPUs [0] for process 0 (actually maps to GPUs [0])
|
| 68 |
+
[2023-09-14 13:48:29,699][43415] Using GPUs [1] for process 1 (actually maps to GPUs [1])
|
| 69 |
+
[2023-09-14 13:48:29,699][43415] Set environment var CUDA_VISIBLE_DEVICES to '1' (GPU indices [1]) for learning process 1
|
| 70 |
+
[2023-09-14 13:48:29,737][43415] Num visible devices: 1
|
| 71 |
+
[2023-09-14 13:48:29,778][43415] Starting seed is not provided
|
| 72 |
+
[2023-09-14 13:48:29,778][43415] Using GPUs [0] for process 1 (actually maps to GPUs [1])
|
| 73 |
+
[2023-09-14 13:48:29,779][43415] Initializing actor-critic model on device cuda:0
|
| 74 |
+
[2023-09-14 13:48:29,779][43415] RunningMeanStd input shape: (23,)
|
| 75 |
+
[2023-09-14 13:48:29,779][43415] RunningMeanStd input shape: (3, 72, 128)
|
| 76 |
+
[2023-09-14 13:48:29,780][43415] RunningMeanStd input shape: (1,)
|
| 77 |
+
[2023-09-14 13:48:29,792][43415] ConvEncoder: input_channels=3
|
| 78 |
+
[2023-09-14 13:48:30,065][43415] Conv encoder output size: 512
|
| 79 |
+
[2023-09-14 13:48:30,066][43415] Policy head output size: 640
|
| 80 |
+
[2023-09-14 13:48:30,095][43415] Created Actor Critic model with architecture:
|
| 81 |
+
[2023-09-14 13:48:30,095][43415] ActorCriticSharedWeights(
|
| 82 |
+
(obs_normalizer): ObservationNormalizer(
|
| 83 |
+
(running_mean_std): RunningMeanStdDictInPlace(
|
| 84 |
+
(running_mean_std): ModuleDict(
|
| 85 |
+
(measurements): RunningMeanStdInPlace()
|
| 86 |
+
(obs): RunningMeanStdInPlace()
|
| 87 |
+
)
|
| 88 |
+
)
|
| 89 |
+
)
|
| 90 |
+
(returns_normalizer): RecursiveScriptModule(original_name=RunningMeanStdInPlace)
|
| 91 |
+
(encoder): VizdoomEncoder(
|
| 92 |
+
(basic_encoder): ConvEncoder(
|
| 93 |
+
(enc): RecursiveScriptModule(
|
| 94 |
+
original_name=ConvEncoderImpl
|
| 95 |
+
(conv_head): RecursiveScriptModule(
|
| 96 |
+
original_name=Sequential
|
| 97 |
+
(0): RecursiveScriptModule(original_name=Conv2d)
|
| 98 |
+
(1): RecursiveScriptModule(original_name=ELU)
|
| 99 |
+
(2): RecursiveScriptModule(original_name=Conv2d)
|
| 100 |
+
(3): RecursiveScriptModule(original_name=ELU)
|
| 101 |
+
(4): RecursiveScriptModule(original_name=Conv2d)
|
| 102 |
+
(5): RecursiveScriptModule(original_name=ELU)
|
| 103 |
+
)
|
| 104 |
+
(mlp_layers): RecursiveScriptModule(
|
| 105 |
+
original_name=Sequential
|
| 106 |
+
(0): RecursiveScriptModule(original_name=Linear)
|
| 107 |
+
(1): RecursiveScriptModule(original_name=ELU)
|
| 108 |
+
)
|
| 109 |
+
)
|
| 110 |
+
)
|
| 111 |
+
(measurements_head): Sequential(
|
| 112 |
+
(0): Linear(in_features=23, out_features=128, bias=True)
|
| 113 |
+
(1): ELU(alpha=1.0)
|
| 114 |
+
(2): Linear(in_features=128, out_features=128, bias=True)
|
| 115 |
+
(3): ELU(alpha=1.0)
|
| 116 |
+
)
|
| 117 |
+
)
|
| 118 |
+
(core): ModelCoreRNN(
|
| 119 |
+
(core): GRU(640, 512)
|
| 120 |
+
)
|
| 121 |
+
(decoder): MlpDecoder(
|
| 122 |
+
(mlp): Identity()
|
| 123 |
+
)
|
| 124 |
+
(critic_linear): Linear(in_features=512, out_features=1, bias=True)
|
| 125 |
+
(action_parameterization): ActionParameterizationDefault(
|
| 126 |
+
(distribution_linear): Linear(in_features=512, out_features=21, bias=True)
|
| 127 |
+
)
|
| 128 |
+
)
|
| 129 |
+
[2023-09-14 13:48:31,315][43415] Using optimizer <class 'torch.optim.adam.Adam'>
|
| 130 |
+
[2023-09-14 13:48:31,316][43415] No checkpoints found
|
| 131 |
+
[2023-09-14 13:48:31,316][43415] Did not load from checkpoint, starting from scratch!
|
| 132 |
+
[2023-09-14 13:48:31,316][43415] Initialized policy 1 weights for model version 0
|
| 133 |
+
[2023-09-14 13:48:31,318][43415] LearnerWorker_p1 finished initialization!
|
| 134 |
+
[2023-09-14 13:48:31,318][43415] Using GPUs [0] for process 1 (actually maps to GPUs [1])
|
| 135 |
+
[2023-09-14 13:48:31,727][43670] Worker 0 uses CPU cores [0, 1, 2, 3]
|
| 136 |
+
[2023-09-14 13:48:31,819][43675] Worker 5 uses CPU cores [20, 21, 22, 23]
|
| 137 |
+
[2023-09-14 13:48:31,825][43672] Worker 2 uses CPU cores [8, 9, 10, 11]
|
| 138 |
+
[2023-09-14 13:48:31,873][43669] Using GPUs [1] for process 1 (actually maps to GPUs [1])
|
| 139 |
+
[2023-09-14 13:48:31,873][43669] Set environment var CUDA_VISIBLE_DEVICES to '1' (GPU indices [1]) for inference process 1
|
| 140 |
+
[2023-09-14 13:48:31,887][43728] Worker 7 uses CPU cores [28, 29, 30, 31]
|
| 141 |
+
[2023-09-14 13:48:31,891][43669] Num visible devices: 1
|
| 142 |
+
[2023-09-14 13:48:32,011][43671] Worker 1 uses CPU cores [4, 5, 6, 7]
|
| 143 |
+
[2023-09-14 13:48:32,225][43673] Worker 3 uses CPU cores [12, 13, 14, 15]
|
| 144 |
+
[2023-09-14 13:48:32,325][43668] Using GPUs [0] for process 0 (actually maps to GPUs [0])
|
| 145 |
+
[2023-09-14 13:48:32,325][43668] Set environment var CUDA_VISIBLE_DEVICES to '0' (GPU indices [0]) for inference process 0
|
| 146 |
+
[2023-09-14 13:48:32,342][43674] Worker 4 uses CPU cores [16, 17, 18, 19]
|
| 147 |
+
[2023-09-14 13:48:32,346][43668] Num visible devices: 1
|
| 148 |
+
[2023-09-14 13:48:32,458][43726] Worker 6 uses CPU cores [24, 25, 26, 27]
|
| 149 |
+
[2023-09-14 13:48:32,525][43669] RunningMeanStd input shape: (23,)
|
| 150 |
+
[2023-09-14 13:48:32,526][43669] RunningMeanStd input shape: (3, 72, 128)
|
| 151 |
+
[2023-09-14 13:48:32,526][43669] RunningMeanStd input shape: (1,)
|
| 152 |
+
[2023-09-14 13:48:32,538][43669] ConvEncoder: input_channels=3
|
| 153 |
+
[2023-09-14 13:48:32,642][43669] Conv encoder output size: 512
|
| 154 |
+
[2023-09-14 13:48:32,643][43669] Policy head output size: 640
|
| 155 |
+
[2023-09-14 13:48:32,955][43668] RunningMeanStd input shape: (23,)
|
| 156 |
+
[2023-09-14 13:48:32,956][43668] RunningMeanStd input shape: (3, 72, 128)
|
| 157 |
+
[2023-09-14 13:48:32,956][43668] RunningMeanStd input shape: (1,)
|
| 158 |
+
[2023-09-14 13:48:32,968][43668] ConvEncoder: input_channels=3
|
| 159 |
+
[2023-09-14 13:48:33,071][43668] Conv encoder output size: 512
|
| 160 |
+
[2023-09-14 13:48:33,072][43668] Policy head output size: 640
|
| 161 |
+
[2023-09-14 13:48:33,386][43670] Doom resolution: 160x120, resize resolution: (128, 72)
|
| 162 |
+
[2023-09-14 13:48:33,394][43674] Doom resolution: 160x120, resize resolution: (128, 72)
|
| 163 |
+
[2023-09-14 13:48:33,394][43728] Doom resolution: 160x120, resize resolution: (128, 72)
|
| 164 |
+
[2023-09-14 13:48:33,403][43673] Doom resolution: 160x120, resize resolution: (128, 72)
|
| 165 |
+
[2023-09-14 13:48:33,403][43671] Doom resolution: 160x120, resize resolution: (128, 72)
|
| 166 |
+
[2023-09-14 13:48:33,404][43675] Doom resolution: 160x120, resize resolution: (128, 72)
|
| 167 |
+
[2023-09-14 13:48:33,412][43672] Doom resolution: 160x120, resize resolution: (128, 72)
|
| 168 |
+
[2023-09-14 13:48:33,412][43726] Doom resolution: 160x120, resize resolution: (128, 72)
|
| 169 |
+
[2023-09-14 13:48:33,711][43728] Decorrelating experience for 0 frames...
|
| 170 |
+
[2023-09-14 13:48:33,735][43671] Decorrelating experience for 0 frames...
|
| 171 |
+
[2023-09-14 13:48:33,756][43726] Decorrelating experience for 0 frames...
|
| 172 |
+
[2023-09-14 13:48:33,781][43670] Decorrelating experience for 0 frames...
|
| 173 |
+
[2023-09-14 13:48:33,795][43674] Decorrelating experience for 0 frames...
|
| 174 |
+
[2023-09-14 13:48:34,021][43671] Decorrelating experience for 32 frames...
|
| 175 |
+
[2023-09-14 13:48:34,052][43672] Decorrelating experience for 0 frames...
|
| 176 |
+
[2023-09-14 13:48:34,101][43726] Decorrelating experience for 32 frames...
|
| 177 |
+
[2023-09-14 13:48:34,102][43673] Decorrelating experience for 0 frames...
|
| 178 |
+
[2023-09-14 13:48:34,104][43674] Decorrelating experience for 32 frames...
|
| 179 |
+
[2023-09-14 13:48:34,354][43670] Decorrelating experience for 32 frames...
|
| 180 |
+
[2023-09-14 13:48:34,377][43672] Decorrelating experience for 32 frames...
|
| 181 |
+
[2023-09-14 13:48:34,467][43675] Decorrelating experience for 0 frames...
|
| 182 |
+
[2023-09-14 13:48:34,528][43671] Decorrelating experience for 64 frames...
|
| 183 |
+
[2023-09-14 13:48:34,547][43726] Decorrelating experience for 64 frames...
|
| 184 |
+
[2023-09-14 13:48:34,637][43673] Decorrelating experience for 32 frames...
|
| 185 |
+
[2023-09-14 13:48:34,760][43728] Decorrelating experience for 32 frames...
|
| 186 |
+
[2023-09-14 13:48:34,827][43672] Decorrelating experience for 64 frames...
|
| 187 |
+
[2023-09-14 13:48:34,926][43670] Decorrelating experience for 64 frames...
|
| 188 |
+
[2023-09-14 13:48:34,929][43726] Decorrelating experience for 96 frames...
|
| 189 |
+
[2023-09-14 13:48:34,954][43675] Decorrelating experience for 32 frames...
|
| 190 |
+
[2023-09-14 13:48:35,000][43671] Decorrelating experience for 96 frames...
|
| 191 |
+
[2023-09-14 13:48:35,104][43674] Decorrelating experience for 64 frames...
|
| 192 |
+
[2023-09-14 13:48:35,125][43728] Decorrelating experience for 64 frames...
|
| 193 |
+
[2023-09-14 13:48:35,184][43673] Decorrelating experience for 64 frames...
|
| 194 |
+
[2023-09-14 13:48:35,347][43672] Decorrelating experience for 96 frames...
|
| 195 |
+
[2023-09-14 13:48:35,472][43675] Decorrelating experience for 64 frames...
|
| 196 |
+
[2023-09-14 13:48:35,503][43674] Decorrelating experience for 96 frames...
|
| 197 |
+
[2023-09-14 13:48:35,554][43670] Decorrelating experience for 96 frames...
|
| 198 |
+
[2023-09-14 13:48:35,597][43673] Decorrelating experience for 96 frames...
|
| 199 |
+
[2023-09-14 13:48:35,771][43728] Decorrelating experience for 96 frames...
|
| 200 |
+
[2023-09-14 13:48:35,917][43675] Decorrelating experience for 96 frames...
|
| 201 |
+
[2023-09-14 13:48:36,328][43671] Multiple policies in trajectory buffer: [0 1] (-1 means inactive agent)
|
| 202 |
+
[2023-09-14 13:48:36,356][43726] Multiple policies in trajectory buffer: [0 1] (-1 means inactive agent)
|
| 203 |
+
[2023-09-14 13:48:36,445][43673] Multiple policies in trajectory buffer: [0 1] (-1 means inactive agent)
|
| 204 |
+
[2023-09-14 13:48:36,698][43728] Multiple policies in trajectory buffer: [0 1] (-1 means inactive agent)
|
| 205 |
+
[2023-09-14 13:48:36,862][43415] Signal inference workers to stop experience collection...
|
| 206 |
+
[2023-09-14 13:48:36,868][43668] InferenceWorker_p0-w0: stopping experience collection
|
| 207 |
+
[2023-09-14 13:48:36,871][43669] InferenceWorker_p1-w0: stopping experience collection
|
| 208 |
+
[2023-09-14 13:48:39,985][43415] Signal inference workers to resume experience collection...
|
| 209 |
+
[2023-09-14 13:48:39,986][43669] InferenceWorker_p1-w0: resuming experience collection
|
| 210 |
+
[2023-09-14 13:48:39,986][43668] InferenceWorker_p0-w0: resuming experience collection
|
| 211 |
+
[2023-09-14 13:48:41,396][43670] Multiple policies in trajectory buffer: [0 1] (-1 means inactive agent)
|
| 212 |
+
[2023-09-14 13:48:41,473][43674] Multiple policies in trajectory buffer: [0 1] (-1 means inactive agent)
|
| 213 |
+
[2023-09-14 13:48:41,497][43675] Multiple policies in trajectory buffer: [0 1] (-1 means inactive agent)
|
| 214 |
+
[2023-09-14 13:48:42,205][43239] Signal inference workers to stop experience collection...
|
| 215 |
+
[2023-09-14 13:48:42,770][43239] Signal inference workers to resume experience collection...
|
| 216 |
+
[2023-09-14 13:48:42,869][43672] Multiple policies in trajectory buffer: [0 1] (-1 means inactive agent)
|
| 217 |
+
[2023-09-14 13:48:45,713][43669] Updated weights for policy 1, policy_version 10 (0.0696)
|
| 218 |
+
[2023-09-14 13:48:46,997][43668] Updated weights for policy 0, policy_version 10 (0.0011)
|
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[2023-09-14 13:48:59,324][43239] Saving new best policy, reward=1.015!
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[2023-09-14 13:48:59,324][43415] Saving new best policy, reward=0.659!
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[2023-09-14 13:49:04,329][43415] Saving new best policy, reward=0.917!
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[2023-09-14 13:49:09,324][43239] Saving new best policy, reward=1.051!
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[2023-09-14 13:49:09,324][43415] Saving new best policy, reward=1.069!
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[2023-09-14 13:49:14,328][43239] Saving new best policy, reward=1.074!
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[2023-09-14 13:49:14,328][43415] Saving new best policy, reward=1.136!
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[2023-09-14 13:49:34,328][43239] Saving new best policy, reward=1.111!
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[2023-09-14 13:49:49,324][43415] Saving new best policy, reward=1.155!
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[2023-09-14 13:49:54,328][43415] Saving new best policy, reward=1.220!
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[2023-09-14 13:49:54,739][43668] Updated weights for policy 0, policy_version 130 (0.0012)
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[2023-09-14 13:49:59,324][43415] Saving new best policy, reward=1.230!
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[2023-09-14 13:49:59,324][43239] Saving new best policy, reward=1.182!
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[2023-09-14 13:50:09,324][43239] Saving new best policy, reward=1.312!
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[2023-09-14 13:50:14,329][43239] Saving new best policy, reward=1.482!
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[2023-09-14 13:50:19,325][43239] Saving new best policy, reward=1.600!
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[2023-09-14 13:50:23,419][43668] Updated weights for policy 0, policy_version 180 (0.0011)
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[2023-09-14 13:50:24,329][43239] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000000181_741376.pth...
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[2023-09-14 13:50:24,331][43415] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p1/checkpoint_000000169_692224.pth...
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[2023-09-14 13:50:24,390][43239] Saving new best policy, reward=1.609!
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[2023-09-14 13:51:14,336][43415] Saving new best policy, reward=1.377!
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[2023-09-14 13:51:15,268][43669] Updated weights for policy 1, policy_version 250 (0.0010)
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[2023-09-14 13:51:19,326][43415] Saving new best policy, reward=1.432!
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[2023-09-14 13:51:23,371][43668] Updated weights for policy 0, policy_version 290 (0.0010)
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[2023-09-14 13:51:34,330][43239] Saving new best policy, reward=1.678!
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[2023-09-14 13:51:39,324][43239] Saving new best policy, reward=1.700!
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[2023-09-14 13:51:40,233][43668] Updated weights for policy 0, policy_version 320 (0.0010)
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[2023-09-14 13:51:44,329][43239] Saving new best policy, reward=1.701!
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[2023-09-14 13:51:54,328][43239] Saving new best policy, reward=1.767!
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[2023-09-14 13:52:24,331][43239] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000000389_1593344.pth...
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[2023-09-14 13:52:24,331][43415] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p1/checkpoint_000000364_1490944.pth...
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[2023-09-14 13:52:24,390][43415] Saving new best policy, reward=1.450!
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[2023-09-14 13:52:39,324][43415] Saving new best policy, reward=1.671!
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[2023-09-14 13:52:44,329][43415] Saving new best policy, reward=1.702!
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[2023-09-14 13:52:49,324][43415] Saving new best policy, reward=1.784!
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[2023-09-14 13:52:54,329][43415] Saving new best policy, reward=1.827!
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[2023-09-14 13:53:14,357][43239] Saving new best policy, reward=1.769!
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[2023-09-14 13:53:19,324][43239] Saving new best policy, reward=1.876!
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[2023-09-14 13:53:54,328][43239] Saving new best policy, reward=1.914!
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[2023-09-14 13:54:24,330][43239] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000000602_2465792.pth...
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[2023-09-14 13:54:24,330][43415] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p1/checkpoint_000000555_2273280.pth...
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[2023-09-14 13:54:24,396][43239] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000000181_741376.pth
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[2023-09-14 13:54:24,397][43415] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p1/checkpoint_000000169_692224.pth
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[2023-09-14 13:54:24,403][43239] Saving new best policy, reward=2.014!
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[2023-09-14 13:54:27,319][43669] Updated weights for policy 1, policy_version 560 (0.0010)
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[2023-09-14 13:54:49,324][43239] Saving new best policy, reward=2.046!
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[2023-09-14 13:54:54,371][43239] Saving new best policy, reward=2.205!
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[2023-09-14 13:54:59,324][43239] Saving new best policy, reward=2.273!
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[2023-09-14 13:55:02,185][43668] Updated weights for policy 0, policy_version 670 (0.0010)
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[2023-09-14 13:55:17,178][43669] Updated weights for policy 1, policy_version 630 (0.0011)
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[2023-09-14 13:55:19,349][43668] Updated weights for policy 0, policy_version 710 (0.0011)
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[2023-09-14 13:55:24,284][43668] Updated weights for policy 0, policy_version 720 (0.0012)
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[2023-09-14 13:55:25,416][43669] Updated weights for policy 1, policy_version 640 (0.0010)
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[2023-09-14 13:55:29,020][43668] Updated weights for policy 0, policy_version 730 (0.0010)
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[2023-09-14 13:55:33,286][43668] Updated weights for policy 0, policy_version 740 (0.0010)
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[2023-09-14 13:55:34,882][43669] Updated weights for policy 1, policy_version 650 (0.0010)
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[2023-09-14 13:55:37,491][43668] Updated weights for policy 0, policy_version 750 (0.0011)
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[2023-09-14 13:55:41,904][43668] Updated weights for policy 0, policy_version 760 (0.0010)
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[2023-09-14 13:55:43,315][43669] Updated weights for policy 1, policy_version 660 (0.0011)
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[2023-09-14 13:55:44,333][43415] Saving new best policy, reward=2.036!
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[2023-09-14 13:55:46,577][43668] Updated weights for policy 0, policy_version 770 (0.0010)
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[2023-09-14 13:55:50,879][43669] Updated weights for policy 1, policy_version 670 (0.0010)
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[2023-09-14 13:55:51,567][43668] Updated weights for policy 0, policy_version 780 (0.0010)
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[2023-09-14 13:55:54,329][43415] Saving new best policy, reward=2.052!
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[2023-09-14 13:55:56,772][43668] Updated weights for policy 0, policy_version 790 (0.0011)
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[2023-09-14 13:55:57,756][43669] Updated weights for policy 1, policy_version 680 (0.0010)
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[2023-09-14 13:56:02,254][43668] Updated weights for policy 0, policy_version 800 (0.0010)
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[2023-09-14 13:56:03,992][43669] Updated weights for policy 1, policy_version 690 (0.0010)
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[2023-09-14 13:56:08,234][43668] Updated weights for policy 0, policy_version 810 (0.0010)
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[2023-09-14 13:56:14,116][43668] Updated weights for policy 0, policy_version 820 (0.0010)
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[2023-09-14 13:56:15,621][43669] Updated weights for policy 1, policy_version 710 (0.0010)
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[2023-09-14 13:56:20,129][43668] Updated weights for policy 0, policy_version 830 (0.0010)
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[2023-09-14 13:56:21,556][43669] Updated weights for policy 1, policy_version 720 (0.0011)
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[2023-09-14 13:56:24,329][43239] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000000837_3428352.pth...
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| 417 |
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[2023-09-14 13:56:24,330][43415] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p1/checkpoint_000000724_2965504.pth...
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| 418 |
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[2023-09-14 13:56:24,385][43415] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p1/checkpoint_000000364_1490944.pth
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[2023-09-14 13:56:24,401][43239] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000000389_1593344.pth
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[2023-09-14 13:56:25,807][43668] Updated weights for policy 0, policy_version 840 (0.0010)
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[2023-09-14 13:56:28,134][43669] Updated weights for policy 1, policy_version 730 (0.0010)
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[2023-09-14 13:56:31,282][43668] Updated weights for policy 0, policy_version 850 (0.0011)
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[2023-09-14 13:56:34,599][43669] Updated weights for policy 1, policy_version 740 (0.0011)
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[2023-09-14 13:56:36,896][43668] Updated weights for policy 0, policy_version 860 (0.0010)
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[2023-09-14 13:56:41,432][43669] Updated weights for policy 1, policy_version 750 (0.0013)
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[2023-09-14 13:56:41,861][43668] Updated weights for policy 0, policy_version 870 (0.0011)
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[2023-09-14 13:56:47,444][43668] Updated weights for policy 0, policy_version 880 (0.0009)
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[2023-09-14 13:56:47,634][43669] Updated weights for policy 1, policy_version 760 (0.0010)
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[2023-09-14 13:56:53,426][43668] Updated weights for policy 0, policy_version 890 (0.0010)
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[2023-09-14 13:56:53,652][43669] Updated weights for policy 1, policy_version 770 (0.0010)
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[2023-09-14 13:56:59,482][43669] Updated weights for policy 1, policy_version 780 (0.0010)
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[2023-09-14 13:56:59,753][43668] Updated weights for policy 0, policy_version 900 (0.0013)
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[2023-09-14 13:57:13,627][43668] Updated weights for policy 0, policy_version 920 (0.0011)
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[2023-09-14 13:57:15,385][43669] Updated weights for policy 1, policy_version 810 (0.0011)
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[2023-09-14 13:57:37,329][43668] Updated weights for policy 0, policy_version 960 (0.0010)
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[2023-09-14 13:57:42,666][43669] Updated weights for policy 1, policy_version 850 (0.0010)
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[2023-09-14 13:57:46,029][43668] Updated weights for policy 0, policy_version 980 (0.0016)
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[2023-09-14 13:57:50,025][43668] Updated weights for policy 0, policy_version 990 (0.0010)
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[2023-09-14 13:57:54,603][43669] Updated weights for policy 1, policy_version 860 (0.0011)
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[2023-09-14 13:57:58,510][43668] Updated weights for policy 0, policy_version 1010 (0.0010)
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[2023-09-14 13:58:03,562][43669] Updated weights for policy 1, policy_version 870 (0.0011)
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[2023-09-14 13:58:13,520][43668] Updated weights for policy 0, policy_version 1040 (0.0010)
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[2023-09-14 13:58:14,328][43239] Saving new best policy, reward=2.304!
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[2023-09-14 13:58:17,395][43669] Updated weights for policy 1, policy_version 890 (0.0010)
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[2023-09-14 13:58:19,287][43668] Updated weights for policy 0, policy_version 1050 (0.0011)
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[2023-09-14 13:58:19,324][43239] Saving new best policy, reward=2.442!
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[2023-09-14 13:58:23,714][43669] Updated weights for policy 1, policy_version 900 (0.0010)
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[2023-09-14 13:58:24,329][43239] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000001058_4333568.pth...
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| 463 |
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[2023-09-14 13:58:24,383][43415] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p1/checkpoint_000000901_3690496.pth...
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| 464 |
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[2023-09-14 13:58:24,390][43239] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000000602_2465792.pth
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[2023-09-14 13:58:24,438][43415] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p1/checkpoint_000000555_2273280.pth
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[2023-09-14 13:58:25,099][43668] Updated weights for policy 0, policy_version 1060 (0.0009)
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[2023-09-14 13:58:29,324][43415] Saving new best policy, reward=2.057!
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[2023-09-14 13:58:30,395][43669] Updated weights for policy 1, policy_version 910 (0.0012)
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[2023-09-14 13:58:30,553][43668] Updated weights for policy 0, policy_version 1070 (0.0010)
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[2023-09-14 13:58:34,329][43415] Saving new best policy, reward=2.138!
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[2023-09-14 13:58:35,767][43668] Updated weights for policy 0, policy_version 1080 (0.0011)
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[2023-09-14 13:58:37,081][43669] Updated weights for policy 1, policy_version 920 (0.0010)
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[2023-09-14 13:58:40,984][43668] Updated weights for policy 0, policy_version 1090 (0.0010)
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[2023-09-14 13:58:43,780][43669] Updated weights for policy 1, policy_version 930 (0.0011)
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[2023-09-14 13:58:50,338][43669] Updated weights for policy 1, policy_version 940 (0.0011)
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[2023-09-14 13:58:51,627][43668] Updated weights for policy 0, policy_version 1110 (0.0011)
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[2023-09-14 13:58:56,854][43669] Updated weights for policy 1, policy_version 950 (0.0011)
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[2023-09-14 14:00:04,330][43239] Saving new best policy, reward=2.557!
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[2023-09-14 14:00:14,329][43239] Saving new best policy, reward=2.746!
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[2023-09-14 14:00:14,574][43668] Updated weights for policy 0, policy_version 1240 (0.0010)
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[2023-09-14 14:00:19,324][43239] Saving new best policy, reward=2.841!
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[2023-09-14 14:00:19,564][43668] Updated weights for policy 0, policy_version 1250 (0.0011)
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[2023-09-14 14:00:22,935][43669] Updated weights for policy 1, policy_version 1100 (0.0011)
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[2023-09-14 14:00:24,329][43239] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000001259_5156864.pth...
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[2023-09-14 14:00:24,330][43415] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p1/checkpoint_000001101_4509696.pth...
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[2023-09-14 14:00:24,387][43239] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000000837_3428352.pth
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[2023-09-14 14:00:24,395][43239] Saving new best policy, reward=2.896!
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[2023-09-14 14:00:24,395][43415] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p1/checkpoint_000000724_2965504.pth
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[2023-09-14 14:00:24,558][43668] Updated weights for policy 0, policy_version 1260 (0.0009)
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[2023-09-14 14:00:39,483][43668] Updated weights for policy 0, policy_version 1290 (0.0010)
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[2023-09-14 14:00:44,503][43669] Updated weights for policy 1, policy_version 1130 (0.0011)
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[2023-09-14 14:01:06,424][43669] Updated weights for policy 1, policy_version 1160 (0.0010)
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[2023-09-14 14:01:13,251][43669] Updated weights for policy 1, policy_version 1170 (0.0012)
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[2023-09-14 14:01:14,329][43239] Saving new best policy, reward=2.903!
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[2023-09-14 14:01:17,503][43668] Updated weights for policy 0, policy_version 1360 (0.0011)
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[2023-09-14 14:01:47,083][43668] Updated weights for policy 0, policy_version 1420 (0.0011)
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[2023-09-14 14:01:48,574][43669] Updated weights for policy 1, policy_version 1220 (0.0010)
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[2023-09-14 14:01:52,129][43668] Updated weights for policy 0, policy_version 1430 (0.0010)
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[2023-09-14 14:01:54,329][43239] Saving new best policy, reward=2.907!
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[2023-09-14 14:01:55,407][43669] Updated weights for policy 1, policy_version 1230 (0.0011)
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[2023-09-14 14:01:56,956][43668] Updated weights for policy 0, policy_version 1440 (0.0011)
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[2023-09-14 14:01:59,324][43239] Saving new best policy, reward=3.081!
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[2023-09-14 14:02:01,872][43668] Updated weights for policy 0, policy_version 1450 (0.0010)
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[2023-09-14 14:02:02,697][43669] Updated weights for policy 1, policy_version 1240 (0.0010)
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[2023-09-14 14:02:07,262][43668] Updated weights for policy 0, policy_version 1460 (0.0011)
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[2023-09-14 14:02:09,010][43669] Updated weights for policy 1, policy_version 1250 (0.0011)
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[2023-09-14 14:02:15,012][43669] Updated weights for policy 1, policy_version 1260 (0.0010)
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[2023-09-14 14:02:15,318][43668] Updated weights for policy 0, policy_version 1470 (0.0014)
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[2023-09-14 14:02:20,855][43669] Updated weights for policy 1, policy_version 1270 (0.0011)
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[2023-09-14 14:02:21,542][43668] Updated weights for policy 0, policy_version 1480 (0.0012)
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| 559 |
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[2023-09-14 14:02:24,328][43239] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000001484_6078464.pth...
|
| 560 |
+
[2023-09-14 14:02:24,328][43415] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p1/checkpoint_000001276_5226496.pth...
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| 561 |
+
[2023-09-14 14:02:24,380][43239] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000001058_4333568.pth
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| 562 |
+
[2023-09-14 14:02:24,384][43415] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p1/checkpoint_000000901_3690496.pth
|
| 563 |
+
[2023-09-14 14:02:26,247][43669] Updated weights for policy 1, policy_version 1280 (0.0010)
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| 564 |
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[2023-09-14 14:02:28,208][43668] Updated weights for policy 0, policy_version 1490 (0.0009)
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[2023-09-14 14:02:31,557][43669] Updated weights for policy 1, policy_version 1290 (0.0009)
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| 566 |
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[2023-09-14 14:02:34,390][43668] Updated weights for policy 0, policy_version 1500 (0.0010)
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[2023-09-14 14:02:37,138][43669] Updated weights for policy 1, policy_version 1300 (0.0010)
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| 568 |
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[2023-09-14 14:02:39,324][43239] Saving new best policy, reward=3.217!
|
| 569 |
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[2023-09-14 14:02:40,217][43668] Updated weights for policy 0, policy_version 1510 (0.0010)
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| 570 |
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[2023-09-14 14:02:43,346][43669] Updated weights for policy 1, policy_version 1310 (0.0011)
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| 571 |
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[2023-09-14 14:02:44,381][43239] Saving new best policy, reward=3.237!
|
| 572 |
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[2023-09-14 14:02:46,057][43668] Updated weights for policy 0, policy_version 1520 (0.0011)
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| 573 |
+
[2023-09-14 14:02:49,324][43239] Saving new best policy, reward=3.414!
|
| 574 |
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[2023-09-14 14:02:49,726][43669] Updated weights for policy 1, policy_version 1320 (0.0010)
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| 575 |
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[2023-09-14 14:02:51,366][43668] Updated weights for policy 0, policy_version 1530 (0.0011)
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| 576 |
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[2023-09-14 14:02:54,329][43239] Saving new best policy, reward=3.605!
|
| 577 |
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[2023-09-14 14:02:56,241][43668] Updated weights for policy 0, policy_version 1540 (0.0011)
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[2023-09-14 14:02:57,382][43669] Updated weights for policy 1, policy_version 1330 (0.0010)
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[2023-09-14 14:03:01,131][43668] Updated weights for policy 0, policy_version 1550 (0.0010)
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[2023-09-14 14:03:05,395][43669] Updated weights for policy 1, policy_version 1340 (0.0010)
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[2023-09-14 14:03:05,700][43668] Updated weights for policy 0, policy_version 1560 (0.0011)
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[2023-09-14 14:03:10,216][43668] Updated weights for policy 0, policy_version 1570 (0.0010)
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[2023-09-14 14:03:13,447][43669] Updated weights for policy 1, policy_version 1350 (0.0011)
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[2023-09-14 14:03:15,329][43668] Updated weights for policy 0, policy_version 1580 (0.0011)
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[2023-09-14 14:03:19,381][43669] Updated weights for policy 1, policy_version 1360 (0.0010)
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[2023-09-14 14:03:21,257][43668] Updated weights for policy 0, policy_version 1590 (0.0011)
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[2023-09-14 14:03:24,748][43669] Updated weights for policy 1, policy_version 1370 (0.0010)
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[2023-09-14 14:03:30,602][43669] Updated weights for policy 1, policy_version 1380 (0.0011)
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[2023-09-14 14:03:33,335][43668] Updated weights for policy 0, policy_version 1610 (0.0011)
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| 591 |
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[2023-09-14 14:03:34,329][43415] Saving new best policy, reward=2.186!
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[2023-09-14 14:03:36,785][43669] Updated weights for policy 1, policy_version 1390 (0.0012)
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[2023-09-14 14:03:39,087][43668] Updated weights for policy 0, policy_version 1620 (0.0011)
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[2023-09-14 14:03:39,324][43239] Saving new best policy, reward=3.657!
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[2023-09-14 14:03:39,338][43415] Saving new best policy, reward=2.286!
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[2023-09-14 14:03:43,206][43669] Updated weights for policy 1, policy_version 1400 (0.0011)
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[2023-09-14 14:03:44,395][43239] Saving new best policy, reward=3.679!
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[2023-09-14 14:03:44,397][43668] Updated weights for policy 0, policy_version 1630 (0.0010)
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[2023-09-14 14:03:49,196][43668] Updated weights for policy 0, policy_version 1640 (0.0010)
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[2023-09-14 14:03:49,324][43239] Saving new best policy, reward=3.955!
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[2023-09-14 14:03:50,986][43669] Updated weights for policy 1, policy_version 1410 (0.0011)
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[2023-09-14 14:03:53,609][43668] Updated weights for policy 0, policy_version 1650 (0.0009)
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[2023-09-14 14:03:54,329][43239] Saving new best policy, reward=4.199!
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[2023-09-14 14:03:57,984][43668] Updated weights for policy 0, policy_version 1660 (0.0010)
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[2023-09-14 14:03:59,895][43669] Updated weights for policy 1, policy_version 1420 (0.0011)
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[2023-09-14 14:04:02,228][43668] Updated weights for policy 0, policy_version 1670 (0.0011)
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| 607 |
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[2023-09-14 14:04:04,328][43239] Saving new best policy, reward=4.518!
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[2023-09-14 14:04:06,386][43668] Updated weights for policy 0, policy_version 1680 (0.0012)
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[2023-09-14 14:04:09,040][43669] Updated weights for policy 1, policy_version 1430 (0.0011)
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[2023-09-14 14:04:09,324][43415] Saving new best policy, reward=2.528!
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[2023-09-14 14:04:10,738][43668] Updated weights for policy 0, policy_version 1690 (0.0010)
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[2023-09-14 14:04:15,337][43668] Updated weights for policy 0, policy_version 1700 (0.0011)
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[2023-09-14 14:04:17,431][43669] Updated weights for policy 1, policy_version 1440 (0.0010)
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[2023-09-14 14:04:20,480][43668] Updated weights for policy 0, policy_version 1710 (0.0010)
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[2023-09-14 14:04:23,985][43669] Updated weights for policy 1, policy_version 1450 (0.0010)
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| 616 |
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[2023-09-14 14:04:24,331][43239] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000001717_7032832.pth...
|
| 617 |
+
[2023-09-14 14:04:24,331][43415] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p1/checkpoint_000001450_5939200.pth...
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| 618 |
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[2023-09-14 14:04:24,412][43239] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000001259_5156864.pth
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[2023-09-14 14:04:24,412][43415] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p1/checkpoint_000001101_4509696.pth
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| 620 |
+
[2023-09-14 14:04:24,421][43415] Saving new best policy, reward=2.671!
|
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[2023-09-14 14:04:25,684][43668] Updated weights for policy 0, policy_version 1720 (0.0010)
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| 622 |
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[2023-09-14 14:04:29,324][43415] Saving new best policy, reward=2.826!
|
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[2023-09-14 14:04:30,083][43669] Updated weights for policy 1, policy_version 1460 (0.0010)
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[2023-09-14 14:04:31,478][43668] Updated weights for policy 0, policy_version 1730 (0.0010)
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[2023-09-14 14:04:37,501][43668] Updated weights for policy 0, policy_version 1740 (0.0011)
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[2023-09-14 14:04:40,997][43669] Updated weights for policy 1, policy_version 1480 (0.0009)
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[2023-09-14 14:04:50,911][43669] Updated weights for policy 1, policy_version 1500 (0.0010)
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[2023-09-14 14:04:54,328][43415] Saving new best policy, reward=3.248!
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[2023-09-14 14:04:56,277][43669] Updated weights for policy 1, policy_version 1510 (0.0011)
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[2023-09-14 14:04:57,517][43668] Updated weights for policy 0, policy_version 1770 (0.0010)
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| 635 |
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[2023-09-14 14:04:59,324][43415] Saving new best policy, reward=3.498!
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[2023-09-14 14:05:01,820][43669] Updated weights for policy 1, policy_version 1520 (0.0010)
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[2023-09-14 14:05:04,331][43415] Saving new best policy, reward=3.723!
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[2023-09-14 14:05:07,829][43669] Updated weights for policy 1, policy_version 1530 (0.0010)
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[2023-09-14 14:05:24,329][43239] Saving new best policy, reward=4.656!
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[2023-09-14 14:05:25,117][43669] Updated weights for policy 1, policy_version 1550 (0.0010)
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[2023-09-14 14:05:27,325][43668] Updated weights for policy 0, policy_version 1830 (0.0011)
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[2023-09-14 14:05:29,324][43239] Saving new best policy, reward=5.256!
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[2023-09-14 14:05:31,523][43668] Updated weights for policy 0, policy_version 1840 (0.0010)
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| 663 |
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[2023-09-14 14:06:09,324][43415] Saving new best policy, reward=3.730!
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[2023-09-14 14:06:14,330][43415] Saving new best policy, reward=3.903!
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| 667 |
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[2023-09-14 14:06:18,729][43668] Updated weights for policy 0, policy_version 1930 (0.0009)
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| 668 |
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[2023-09-14 14:06:19,324][43415] Saving new best policy, reward=4.155!
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[2023-09-14 14:06:20,153][43669] Updated weights for policy 1, policy_version 1630 (0.0011)
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| 670 |
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[2023-09-14 14:06:24,329][43239] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000001939_7942144.pth...
|
| 671 |
+
[2023-09-14 14:06:24,358][43415] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p1/checkpoint_000001637_6705152.pth...
|
| 672 |
+
[2023-09-14 14:06:24,386][43239] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000001484_6078464.pth
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[2023-09-14 14:06:24,415][43415] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p1/checkpoint_000001276_5226496.pth
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[2023-09-14 14:06:24,479][43668] Updated weights for policy 0, policy_version 1940 (0.0010)
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[2023-09-14 14:06:26,261][43669] Updated weights for policy 1, policy_version 1640 (0.0010)
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| 676 |
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[2023-09-14 14:06:29,326][43415] Saving new best policy, reward=4.249!
|
| 677 |
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[2023-09-14 14:06:29,845][43668] Updated weights for policy 0, policy_version 1950 (0.0010)
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| 678 |
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[2023-09-14 14:06:32,623][43669] Updated weights for policy 1, policy_version 1650 (0.0011)
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| 679 |
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[2023-09-14 14:06:34,330][43415] Saving new best policy, reward=4.344!
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+
[2023-09-14 14:06:35,480][43668] Updated weights for policy 0, policy_version 1960 (0.0010)
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| 681 |
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[2023-09-14 14:06:38,290][43669] Updated weights for policy 1, policy_version 1660 (0.0011)
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[2023-09-14 14:06:41,588][43668] Updated weights for policy 0, policy_version 1970 (0.0010)
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[2023-09-14 14:06:43,979][43669] Updated weights for policy 1, policy_version 1670 (0.0010)
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| 684 |
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[2023-09-14 14:06:44,329][43415] Saving new best policy, reward=4.591!
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| 685 |
+
[2023-09-14 14:06:47,329][43668] Updated weights for policy 0, policy_version 1980 (0.0011)
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| 686 |
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[2023-09-14 14:06:49,325][43415] Saving new best policy, reward=4.697!
|
| 687 |
+
[2023-09-14 14:06:50,349][43669] Updated weights for policy 1, policy_version 1680 (0.0010)
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| 688 |
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[2023-09-14 14:06:52,751][43668] Updated weights for policy 0, policy_version 1990 (0.0010)
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| 689 |
+
[2023-09-14 14:06:54,330][43415] Saving new best policy, reward=4.854!
|
| 690 |
+
[2023-09-14 14:06:57,662][43669] Updated weights for policy 1, policy_version 1690 (0.0010)
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| 691 |
+
[2023-09-14 14:06:57,761][43668] Updated weights for policy 0, policy_version 2000 (0.0010)
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[2023-09-14 14:07:02,336][43668] Updated weights for policy 0, policy_version 2010 (0.0010)
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| 693 |
+
[2023-09-14 14:07:04,331][43415] Saving new best policy, reward=4.907!
|
| 694 |
+
[2023-09-14 14:07:06,607][43668] Updated weights for policy 0, policy_version 2020 (0.0010)
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| 695 |
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[2023-09-14 14:07:07,024][43669] Updated weights for policy 1, policy_version 1700 (0.0012)
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[2023-09-14 14:07:11,131][43668] Updated weights for policy 0, policy_version 2030 (0.0010)
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[2023-09-14 14:07:15,677][43669] Updated weights for policy 1, policy_version 1710 (0.0010)
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[2023-09-14 14:07:15,872][43668] Updated weights for policy 0, policy_version 2040 (0.0010)
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[2023-09-14 14:07:20,539][43668] Updated weights for policy 0, policy_version 2050 (0.0011)
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[2023-09-14 14:07:23,590][43669] Updated weights for policy 1, policy_version 1720 (0.0010)
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[2023-09-14 14:07:25,341][43668] Updated weights for policy 0, policy_version 2060 (0.0010)
|
| 702 |
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[2023-09-14 14:07:29,325][43239] Saving new best policy, reward=5.297!
|
| 703 |
+
[2023-09-14 14:07:30,256][43668] Updated weights for policy 0, policy_version 2070 (0.0010)
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| 704 |
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[2023-09-14 14:07:30,446][43669] Updated weights for policy 1, policy_version 1730 (0.0010)
|
| 705 |
+
[2023-09-14 14:07:34,328][43239] Saving new best policy, reward=5.849!
|
| 706 |
+
[2023-09-14 14:07:35,470][43668] Updated weights for policy 0, policy_version 2080 (0.0011)
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| 707 |
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[2023-09-14 14:07:37,088][43669] Updated weights for policy 1, policy_version 1740 (0.0011)
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| 708 |
+
[2023-09-14 14:07:39,324][43239] Saving new best policy, reward=5.875!
|
| 709 |
+
[2023-09-14 14:07:41,059][43668] Updated weights for policy 0, policy_version 2090 (0.0012)
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| 710 |
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[2023-09-14 14:07:43,191][43669] Updated weights for policy 1, policy_version 1750 (0.0011)
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[2023-09-14 14:07:46,409][43668] Updated weights for policy 0, policy_version 2100 (0.0010)
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| 712 |
+
[2023-09-14 14:07:49,377][43239] Saving new best policy, reward=6.033!
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| 713 |
+
[2023-09-14 14:07:50,130][43669] Updated weights for policy 1, policy_version 1760 (0.0010)
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| 714 |
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[2023-09-14 14:07:51,362][43668] Updated weights for policy 0, policy_version 2110 (0.0011)
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| 715 |
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[2023-09-14 14:07:54,329][43239] Saving new best policy, reward=6.162!
|
| 716 |
+
[2023-09-14 14:07:55,955][43668] Updated weights for policy 0, policy_version 2120 (0.0010)
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| 717 |
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[2023-09-14 14:07:57,963][43669] Updated weights for policy 1, policy_version 1770 (0.0011)
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[2023-09-14 14:08:00,239][43668] Updated weights for policy 0, policy_version 2130 (0.0010)
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[2023-09-14 14:08:04,504][43668] Updated weights for policy 0, policy_version 2140 (0.0010)
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[2023-09-14 14:08:08,635][43668] Updated weights for policy 0, policy_version 2150 (0.0011)
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[2023-09-14 14:08:12,606][43668] Updated weights for policy 0, policy_version 2160 (0.0011)
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[2023-09-14 14:08:16,598][43668] Updated weights for policy 0, policy_version 2170 (0.0011)
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[2023-09-14 14:08:17,167][43669] Updated weights for policy 1, policy_version 1790 (0.0011)
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[2023-09-14 14:08:24,215][43668] Updated weights for policy 0, policy_version 2190 (0.0011)
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| 727 |
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[2023-09-14 14:08:24,329][43415] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p1/checkpoint_000001796_7356416.pth...
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| 728 |
+
[2023-09-14 14:08:24,330][43239] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000002190_8970240.pth...
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| 729 |
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[2023-09-14 14:08:24,389][43415] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p1/checkpoint_000001450_5939200.pth
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[2023-09-14 14:08:24,391][43239] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000001717_7032832.pth
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[2023-09-14 14:08:27,510][43669] Updated weights for policy 1, policy_version 1800 (0.0011)
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[2023-09-14 14:08:28,276][43668] Updated weights for policy 0, policy_version 2200 (0.0010)
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[2023-09-14 14:08:35,856][43669] Updated weights for policy 1, policy_version 1810 (0.0011)
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[2023-09-14 14:08:52,659][43669] Updated weights for policy 1, policy_version 1830 (0.0010)
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[2023-09-14 14:08:56,179][43668] Updated weights for policy 0, policy_version 2260 (0.0011)
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[2023-09-14 14:08:59,325][43239] Saving new best policy, reward=6.441!
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[2023-09-14 14:08:59,810][43669] Updated weights for policy 1, policy_version 1840 (0.0010)
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[2023-09-14 14:09:06,255][43669] Updated weights for policy 1, policy_version 1850 (0.0010)
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[2023-09-14 14:09:12,812][43669] Updated weights for policy 1, policy_version 1860 (0.0010)
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[2023-09-14 14:09:18,874][43669] Updated weights for policy 1, policy_version 1870 (0.0010)
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[2023-09-14 14:09:24,291][43669] Updated weights for policy 1, policy_version 1880 (0.0010)
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| 759 |
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[2023-09-14 14:09:44,328][43415] Saving new best policy, reward=5.063!
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[2023-09-14 14:09:45,925][43669] Updated weights for policy 1, policy_version 1920 (0.0011)
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[2023-09-14 14:09:49,327][43239] Saving new best policy, reward=6.562!
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[2023-09-14 14:09:49,878][43668] Updated weights for policy 0, policy_version 2350 (0.0011)
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[2023-09-14 14:09:51,789][43669] Updated weights for policy 1, policy_version 1930 (0.0010)
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[2023-09-14 14:09:54,329][43239] Saving new best policy, reward=6.657!
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[2023-09-14 14:09:56,505][43668] Updated weights for policy 0, policy_version 2360 (0.0011)
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[2023-09-14 14:09:59,324][43239] Saving new best policy, reward=6.858!
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| 768 |
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[2023-09-14 14:10:03,057][43668] Updated weights for policy 0, policy_version 2370 (0.0011)
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[2023-09-14 14:10:03,297][43669] Updated weights for policy 1, policy_version 1950 (0.0011)
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[2023-09-14 14:10:04,350][43239] Saving new best policy, reward=6.891!
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[2023-09-14 14:10:09,112][43668] Updated weights for policy 0, policy_version 2380 (0.0010)
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[2023-09-14 14:10:21,091][43669] Updated weights for policy 1, policy_version 1980 (0.0012)
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| 777 |
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[2023-09-14 14:10:24,329][43239] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000002405_9850880.pth...
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| 778 |
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[2023-09-14 14:10:24,329][43415] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p1/checkpoint_000001985_8130560.pth...
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| 779 |
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[2023-09-14 14:10:24,392][43239] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000001939_7942144.pth
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| 780 |
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[2023-09-14 14:10:24,399][43415] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p1/checkpoint_000001637_6705152.pth
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[2023-09-14 14:10:26,867][43669] Updated weights for policy 1, policy_version 1990 (0.0010)
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[2023-09-14 14:10:26,962][43668] Updated weights for policy 0, policy_version 2410 (0.0011)
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[2023-09-14 14:10:32,579][43669] Updated weights for policy 1, policy_version 2000 (0.0011)
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[2023-09-14 14:10:38,630][43669] Updated weights for policy 1, policy_version 2010 (0.0011)
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[2023-09-14 14:10:38,934][43668] Updated weights for policy 0, policy_version 2430 (0.0011)
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[2023-09-14 14:10:39,324][43415] Saving new best policy, reward=5.563!
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[2023-09-14 14:10:44,330][43415] Saving new best policy, reward=5.930!
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[2023-09-14 14:10:44,479][43669] Updated weights for policy 1, policy_version 2020 (0.0011)
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[2023-09-14 14:10:49,324][43415] Saving new best policy, reward=6.351!
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[2023-09-14 14:10:50,287][43669] Updated weights for policy 1, policy_version 2030 (0.0009)
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[2023-09-14 14:10:54,330][43415] Saving new best policy, reward=6.748!
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[2023-09-14 14:10:55,818][43669] Updated weights for policy 1, policy_version 2040 (0.0010)
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[2023-09-14 14:11:01,204][43669] Updated weights for policy 1, policy_version 2050 (0.0011)
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[2023-09-14 14:12:24,330][43239] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000002618_10723328.pth...
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| 826 |
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[2023-09-14 14:12:24,330][43415] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p1/checkpoint_000002179_8925184.pth...
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[2023-09-14 14:12:24,392][43415] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p1/checkpoint_000001796_7356416.pth
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[2023-09-14 14:12:24,392][43239] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000002190_8970240.pth
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| 829 |
+
[2023-09-14 14:12:24,399][43415] Saving new best policy, reward=6.862!
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| 830 |
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[2023-09-14 14:12:24,918][43669] Updated weights for policy 1, policy_version 2180 (0.0010)
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[2023-09-14 14:12:29,324][43415] Saving new best policy, reward=7.037!
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[2023-09-14 14:12:30,769][43669] Updated weights for policy 1, policy_version 2190 (0.0011)
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[2023-09-14 14:12:34,330][43415] Saving new best policy, reward=7.231!
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[2023-09-14 14:12:36,493][43669] Updated weights for policy 1, policy_version 2200 (0.0011)
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[2023-09-14 14:12:39,328][43415] Saving new best policy, reward=7.554!
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[2023-09-14 14:12:42,128][43669] Updated weights for policy 1, policy_version 2210 (0.0010)
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[2023-09-14 14:12:53,386][43669] Updated weights for policy 1, policy_version 2230 (0.0010)
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[2023-09-14 14:12:59,589][43669] Updated weights for policy 1, policy_version 2240 (0.0010)
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[2023-09-14 14:13:05,423][43669] Updated weights for policy 1, policy_version 2250 (0.0010)
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[2023-09-14 14:13:13,846][43668] Updated weights for policy 0, policy_version 2700 (0.0011)
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[2023-09-14 14:13:25,940][43668] Updated weights for policy 0, policy_version 2720 (0.0011)
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[2023-09-14 14:13:27,244][43669] Updated weights for policy 1, policy_version 2290 (0.0011)
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[2023-09-14 14:13:31,252][43668] Updated weights for policy 0, policy_version 2730 (0.0010)
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[2023-09-14 14:13:33,521][43669] Updated weights for policy 1, policy_version 2300 (0.0010)
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| 858 |
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[2023-09-14 14:13:34,330][43415] Saving new best policy, reward=7.608!
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[2023-09-14 14:13:36,369][43668] Updated weights for policy 0, policy_version 2740 (0.0011)
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[2023-09-14 14:13:40,088][43669] Updated weights for policy 1, policy_version 2310 (0.0010)
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[2023-09-14 14:13:41,556][43668] Updated weights for policy 0, policy_version 2750 (0.0010)
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[2023-09-14 14:13:44,329][43415] Saving new best policy, reward=7.796!
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| 863 |
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[2023-09-14 14:13:46,341][43669] Updated weights for policy 1, policy_version 2320 (0.0010)
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| 864 |
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[2023-09-14 14:13:46,972][43668] Updated weights for policy 0, policy_version 2760 (0.0010)
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[2023-09-14 14:13:49,324][43415] Saving new best policy, reward=7.836!
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[2023-09-14 14:13:51,948][43668] Updated weights for policy 0, policy_version 2770 (0.0010)
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[2023-09-14 14:13:53,199][43669] Updated weights for policy 1, policy_version 2330 (0.0010)
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| 868 |
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[2023-09-14 14:13:54,329][43415] Saving new best policy, reward=7.995!
|
| 869 |
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[2023-09-14 14:13:57,247][43668] Updated weights for policy 0, policy_version 2780 (0.0011)
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| 870 |
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[2023-09-14 14:13:59,324][43415] Saving new best policy, reward=8.055!
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| 871 |
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[2023-09-14 14:13:59,620][43669] Updated weights for policy 1, policy_version 2340 (0.0010)
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[2023-09-14 14:14:02,504][43668] Updated weights for policy 0, policy_version 2790 (0.0010)
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| 873 |
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[2023-09-14 14:14:04,330][43415] Saving new best policy, reward=8.121!
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| 874 |
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[2023-09-14 14:14:06,490][43669] Updated weights for policy 1, policy_version 2350 (0.0010)
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| 875 |
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[2023-09-14 14:14:07,319][43668] Updated weights for policy 0, policy_version 2800 (0.0010)
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| 876 |
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[2023-09-14 14:14:09,325][43415] Saving new best policy, reward=8.260!
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| 877 |
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[2023-09-14 14:14:11,961][43668] Updated weights for policy 0, policy_version 2810 (0.0010)
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[2023-09-14 14:14:14,081][43669] Updated weights for policy 1, policy_version 2360 (0.0011)
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[2023-09-14 14:14:16,729][43668] Updated weights for policy 0, policy_version 2820 (0.0010)
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[2023-09-14 14:14:21,332][43668] Updated weights for policy 0, policy_version 2830 (0.0010)
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[2023-09-14 14:14:22,188][43669] Updated weights for policy 1, policy_version 2370 (0.0012)
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| 882 |
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[2023-09-14 14:14:24,330][43415] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p1/checkpoint_000002372_9715712.pth...
|
| 883 |
+
[2023-09-14 14:14:24,330][43239] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000002837_11620352.pth...
|
| 884 |
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[2023-09-14 14:14:24,390][43415] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p1/checkpoint_000001985_8130560.pth
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| 885 |
+
[2023-09-14 14:14:24,390][43239] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000002405_9850880.pth
|
| 886 |
+
[2023-09-14 14:14:25,556][43668] Updated weights for policy 0, policy_version 2840 (0.0010)
|
| 887 |
+
[2023-09-14 14:14:29,687][43668] Updated weights for policy 0, policy_version 2850 (0.0010)
|
| 888 |
+
[2023-09-14 14:14:32,262][43669] Updated weights for policy 1, policy_version 2380 (0.0010)
|
| 889 |
+
[2023-09-14 14:14:33,713][43668] Updated weights for policy 0, policy_version 2860 (0.0010)
|
| 890 |
+
[2023-09-14 14:14:37,914][43668] Updated weights for policy 0, policy_version 2870 (0.0010)
|
| 891 |
+
[2023-09-14 14:14:41,966][43668] Updated weights for policy 0, policy_version 2880 (0.0010)
|
| 892 |
+
[2023-09-14 14:14:43,014][43669] Updated weights for policy 1, policy_version 2390 (0.0010)
|
| 893 |
+
[2023-09-14 14:14:44,329][43239] Saving new best policy, reward=6.896!
|
| 894 |
+
[2023-09-14 14:14:45,883][43668] Updated weights for policy 0, policy_version 2890 (0.0011)
|
| 895 |
+
[2023-09-14 14:14:49,919][43668] Updated weights for policy 0, policy_version 2900 (0.0010)
|
| 896 |
+
[2023-09-14 14:14:52,850][43669] Updated weights for policy 1, policy_version 2400 (0.0011)
|
| 897 |
+
[2023-09-14 14:14:54,189][43668] Updated weights for policy 0, policy_version 2910 (0.0010)
|
| 898 |
+
[2023-09-14 14:14:54,328][43239] Saving new best policy, reward=7.266!
|
| 899 |
+
[2023-09-14 14:14:59,384][43239] Saving new best policy, reward=7.369!
|
| 900 |
+
[2023-09-14 14:14:59,385][43668] Updated weights for policy 0, policy_version 2920 (0.0010)
|
| 901 |
+
[2023-09-14 14:14:59,878][43669] Updated weights for policy 1, policy_version 2410 (0.0011)
|
| 902 |
+
[2023-09-14 14:15:04,329][43239] Saving new best policy, reward=7.444!
|
| 903 |
+
[2023-09-14 14:15:04,576][43668] Updated weights for policy 0, policy_version 2930 (0.0011)
|
| 904 |
+
[2023-09-14 14:15:06,151][43669] Updated weights for policy 1, policy_version 2420 (0.0010)
|
| 905 |
+
[2023-09-14 14:15:09,358][43239] Saving new best policy, reward=7.643!
|
| 906 |
+
[2023-09-14 14:15:10,687][43668] Updated weights for policy 0, policy_version 2940 (0.0010)
|
| 907 |
+
[2023-09-14 14:15:11,436][43669] Updated weights for policy 1, policy_version 2430 (0.0011)
|
| 908 |
+
[2023-09-14 14:15:14,329][43239] Saving new best policy, reward=7.752!
|
| 909 |
+
[2023-09-14 14:15:16,324][43669] Updated weights for policy 1, policy_version 2440 (0.0010)
|
| 910 |
+
[2023-09-14 14:15:17,568][43415] Stopping Batcher_1...
|
| 911 |
+
[2023-09-14 14:15:17,568][43239] Stopping Batcher_0...
|
| 912 |
+
[2023-09-14 14:15:17,568][43239] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000002950_12083200.pth...
|
| 913 |
+
[2023-09-14 14:15:17,569][43415] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p1/checkpoint_000002442_10002432.pth...
|
| 914 |
+
[2023-09-14 14:15:17,569][43668] Updated weights for policy 0, policy_version 2950 (0.0009)
|
| 915 |
+
[2023-09-14 14:15:17,569][43239] Loop batcher_evt_loop terminating...
|
| 916 |
+
[2023-09-14 14:15:17,569][43415] Loop batcher_evt_loop terminating...
|
| 917 |
+
[2023-09-14 14:15:17,583][43671] Stopping RolloutWorker_w1...
|
| 918 |
+
[2023-09-14 14:15:17,583][43670] Stopping RolloutWorker_w0...
|
| 919 |
+
[2023-09-14 14:15:17,583][43675] Stopping RolloutWorker_w5...
|
| 920 |
+
[2023-09-14 14:15:17,584][43728] Stopping RolloutWorker_w7...
|
| 921 |
+
[2023-09-14 14:15:17,584][43671] Loop rollout_proc1_evt_loop terminating...
|
| 922 |
+
[2023-09-14 14:15:17,584][43672] Stopping RolloutWorker_w2...
|
| 923 |
+
[2023-09-14 14:15:17,584][43670] Loop rollout_proc0_evt_loop terminating...
|
| 924 |
+
[2023-09-14 14:15:17,584][43675] Loop rollout_proc5_evt_loop terminating...
|
| 925 |
+
[2023-09-14 14:15:17,584][43728] Loop rollout_proc7_evt_loop terminating...
|
| 926 |
+
[2023-09-14 14:15:17,584][43672] Loop rollout_proc2_evt_loop terminating...
|
| 927 |
+
[2023-09-14 14:15:17,587][43669] Weights refcount: 2 0
|
| 928 |
+
[2023-09-14 14:15:17,587][43668] Weights refcount: 2 0
|
| 929 |
+
[2023-09-14 14:15:17,588][43726] Stopping RolloutWorker_w6...
|
| 930 |
+
[2023-09-14 14:15:17,588][43726] Loop rollout_proc6_evt_loop terminating...
|
| 931 |
+
[2023-09-14 14:15:17,588][43673] Stopping RolloutWorker_w3...
|
| 932 |
+
[2023-09-14 14:15:17,589][43673] Loop rollout_proc3_evt_loop terminating...
|
| 933 |
+
[2023-09-14 14:15:17,589][43669] Stopping InferenceWorker_p1-w0...
|
| 934 |
+
[2023-09-14 14:15:17,589][43668] Stopping InferenceWorker_p0-w0...
|
| 935 |
+
[2023-09-14 14:15:17,589][43669] Loop inference_proc1-0_evt_loop terminating...
|
| 936 |
+
[2023-09-14 14:15:17,589][43668] Loop inference_proc0-0_evt_loop terminating...
|
| 937 |
+
[2023-09-14 14:15:17,593][43674] Stopping RolloutWorker_w4...
|
| 938 |
+
[2023-09-14 14:15:17,594][43674] Loop rollout_proc4_evt_loop terminating...
|
| 939 |
+
[2023-09-14 14:15:17,639][43415] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p1/checkpoint_000002179_8925184.pth
|
| 940 |
+
[2023-09-14 14:15:17,643][43239] Removing /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000002618_10723328.pth
|
| 941 |
+
[2023-09-14 14:15:17,650][43415] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p1/checkpoint_000002442_10002432.pth...
|
| 942 |
+
[2023-09-14 14:15:17,653][43239] Saving new best policy, reward=7.781!
|
| 943 |
+
[2023-09-14 14:15:17,741][43239] Saving /home/cogstack/Documents/optuna/environments/sample_factory/train_dir/default_experiment/checkpoint_p0/checkpoint_000002950_12083200.pth...
|
| 944 |
+
[2023-09-14 14:15:17,745][43415] Stopping LearnerWorker_p1...
|
| 945 |
+
[2023-09-14 14:15:17,746][43415] Loop learner_proc1_evt_loop terminating...
|
| 946 |
+
[2023-09-14 14:15:17,837][43239] Stopping LearnerWorker_p0...
|
| 947 |
+
[2023-09-14 14:15:17,837][43239] Loop learner_proc0_evt_loop terminating...
|