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Browse files- .summary/0/events.out.tfevents.1697047556.rhmmedcatt-proliant-ml350-gen10 +3 -0
- .summary/1/events.out.tfevents.1697047556.rhmmedcatt-proliant-ml350-gen10 +3 -0
- README.md +117 -4
- checkpoint_p0/best_000042880_43909120_reward_337.550.pth +3 -0
- checkpoint_p0/checkpoint_000097024_99352576.pth +3 -0
- checkpoint_p0/checkpoint_000097760_100106240.pth +3 -0
- checkpoint_p0/milestones/checkpoint_000008384_8585216.pth +3 -0
- checkpoint_p0/milestones/checkpoint_000016896_17301504.pth +3 -0
- checkpoint_p0/milestones/checkpoint_000025408_26017792.pth +3 -0
- checkpoint_p0/milestones/checkpoint_000033952_34766848.pth +3 -0
- checkpoint_p0/milestones/checkpoint_000042464_43483136.pth +3 -0
- checkpoint_p0/milestones/checkpoint_000051008_52232192.pth +3 -0
- checkpoint_p0/milestones/checkpoint_000059552_60981248.pth +3 -0
- checkpoint_p0/milestones/checkpoint_000068064_69697536.pth +3 -0
- checkpoint_p0/milestones/checkpoint_000076544_78381056.pth +3 -0
- checkpoint_p0/milestones/checkpoint_000085056_87097344.pth +3 -0
- checkpoint_p0/milestones/checkpoint_000093600_95846400.pth +3 -0
- checkpoint_p1/best_000037152_38043648_reward_191.360.pth +3 -0
- checkpoint_p1/checkpoint_000096928_99254272.pth +3 -0
- checkpoint_p1/checkpoint_000097696_100040704.pth +3 -0
- checkpoint_p1/milestones/checkpoint_000008384_8585216.pth +3 -0
- checkpoint_p1/milestones/checkpoint_000016864_17268736.pth +3 -0
- checkpoint_p1/milestones/checkpoint_000025376_25985024.pth +3 -0
- checkpoint_p1/milestones/checkpoint_000033920_34734080.pth +3 -0
- checkpoint_p1/milestones/checkpoint_000042464_43483136.pth +3 -0
- checkpoint_p1/milestones/checkpoint_000050976_52199424.pth +3 -0
- checkpoint_p1/milestones/checkpoint_000059488_60915712.pth +3 -0
- checkpoint_p1/milestones/checkpoint_000068000_69632000.pth +3 -0
- checkpoint_p1/milestones/checkpoint_000076480_78315520.pth +3 -0
- checkpoint_p1/milestones/checkpoint_000084992_87031808.pth +3 -0
- checkpoint_p1/milestones/checkpoint_000093504_95748096.pth +3 -0
- config.json +40 -22
- git.diff +2 -2
- replay.mp4 +2 -2
- sf_log.txt +0 -0
.summary/0/events.out.tfevents.1697047556.rhmmedcatt-proliant-ml350-gen10
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README.md
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@@ -15,7 +15,7 @@ model-index:
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type: atari_gopher
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metrics:
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- type: mean_reward
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-
value:
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name: mean_reward
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verified: false
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---
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@@ -30,15 +30,128 @@ Documentation for how to use Sample-Factory can be found at https://www.samplefa
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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/
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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 sf_examples.atari.enjoy_atari --algo=APPO --env=atari_gopher --train_dir=./train_dir --experiment=
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```
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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 sf_examples.atari.train_atari --algo=APPO --env=atari_gopher --train_dir=./train_dir --experiment=
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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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type: atari_gopher
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| 16 |
metrics:
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| 17 |
- type: mean_reward
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+
value: 10948.00 +/- 4990.36
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| 19 |
name: mean_reward
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| 20 |
verified: false
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| 21 |
---
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| 30 |
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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/APPO-atari_gopher
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```
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+
## About the Model
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This model as with all the others in the benchmarks was trained initially asynchronously un-seeded to 10 million steps for the purposes of setting a sample factory async baseline for this model on this environment but only 3/57 made it.
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The aim is to reach state-of-the-art (SOTA) performance on each atari environment. I will flag the models with SOTA when they reach at or near these levels.
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The hyperparameters used in the model are the ones I have pushed to my fork of sample-factory: https://github.com/MattStammers/sample-factory. Given that https://huggingface.co/edbeeching has kindly shared his.
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I saved time and energy by using many of his tuned hyperparameters to maximise performance. However, he used 2 billion training steps. I have started as explained above at 10 million then moved to 100m to see how performance goes:
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```
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hyperparameters = {
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"device": "gpu",
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"seed": 1234,
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"num_policies": 2,
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"async_rl": true,
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"serial_mode": false,
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"batched_sampling": true,
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"num_batches_to_accumulate": 2,
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"worker_num_splits": 1,
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"policy_workers_per_policy": 1,
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"max_policy_lag": 1000,
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"num_workers": 16,
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"num_envs_per_worker": 2,
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"batch_size": 1024,
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"num_batches_per_epoch": 8,
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"num_epochs": 4,
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"rollout": 128,
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"recurrence": 1,
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"shuffle_minibatches": false,
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"gamma": 0.99,
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"reward_scale": 1.0,
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"reward_clip": 1000.0,
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"value_bootstrap": false,
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"normalize_returns": true,
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+
"exploration_loss_coeff": 0.0004677351413,
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+
"value_loss_coeff": 0.5,
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"kl_loss_coeff": 0.0,
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"exploration_loss": "entropy",
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"gae_lambda": 0.95,
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| 75 |
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"ppo_clip_ratio": 0.1,
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"ppo_clip_value": 1.0,
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"with_vtrace": false,
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"vtrace_rho": 1.0,
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"vtrace_c": 1.0,
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"optimizer": "adam",
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"adam_eps": 1e-05,
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"adam_beta1": 0.9,
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"adam_beta2": 0.999,
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"max_grad_norm": 0.0,
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"learning_rate": 0.0003033891184,
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+
"lr_schedule": "linear_decay",
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+
"lr_schedule_kl_threshold": 0.008,
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+
"lr_adaptive_min": 1e-06,
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"lr_adaptive_max": 0.01,
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+
"obs_subtract_mean": 0.0,
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"obs_scale": 255.0,
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"normalize_input": true,
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"normalize_input_keys": [
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"obs"
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],
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"decorrelate_experience_max_seconds": 0,
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"decorrelate_envs_on_one_worker": true,
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"actor_worker_gpus": [],
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"set_workers_cpu_affinity": true,
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+
"force_envs_single_thread": false,
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"default_niceness": 0,
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"log_to_file": true,
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+
"experiment_summaries_interval": 3,
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+
"flush_summaries_interval": 30,
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"stats_avg": 100,
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+
"summaries_use_frameskip": true,
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"heartbeat_interval": 10,
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"heartbeat_reporting_interval": 60,
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+
"train_for_env_steps": 100000000,
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"train_for_seconds": 10000000000,
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+
"save_every_sec": 120,
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"keep_checkpoints": 2,
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"load_checkpoint_kind": "latest",
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"save_milestones_sec": 1200,
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"save_best_every_sec": 5,
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"save_best_metric": "reward",
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"save_best_after": 100000,
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"benchmark": false,
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"encoder_mlp_layers": [
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512,
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512
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],
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"encoder_conv_architecture": "convnet_atari",
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"encoder_conv_mlp_layers": [
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512
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],
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"use_rnn": false,
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"rnn_size": 512,
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"rnn_type": "gru",
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"rnn_num_layers": 1,
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"decoder_mlp_layers": [],
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"nonlinearity": "relu",
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"policy_initialization": "orthogonal",
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"policy_init_gain": 1.0,
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+
"actor_critic_share_weights": true,
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"adaptive_stddev": false,
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"continuous_tanh_scale": 0.0,
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"initial_stddev": 1.0,
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"use_env_info_cache": false,
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"env_gpu_actions": false,
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"env_gpu_observations": true,
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"env_frameskip": 4,
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+
"env_framestack": 4,
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}
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+
```
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+
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+
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+
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## Using the model
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| 151 |
|
| 152 |
To run the model after download, use the `enjoy` script corresponding to this environment:
|
| 153 |
```
|
| 154 |
+
python -m sf_examples.atari.enjoy_atari --algo=APPO --env=atari_gopher --train_dir=./train_dir --experiment=APPO-atari_gopher
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| 155 |
```
|
| 156 |
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To continue training with this model, use the `train` script corresponding to this environment:
|
| 164 |
```
|
| 165 |
+
python -m sf_examples.atari.train_atari --algo=APPO --env=atari_gopher --train_dir=./train_dir --experiment=APPO-atari_gopher --restart_behavior=resume --train_for_env_steps=10000000000
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```
|
| 167 |
|
| 168 |
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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config.json
CHANGED
|
@@ -2,23 +2,23 @@
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|
| 2 |
"help": false,
|
| 3 |
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|
| 4 |
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|
| 5 |
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|
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| 11 |
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| 12 |
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| 13 |
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| 14 |
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|
| 15 |
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|
| 16 |
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|
| 17 |
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|
| 18 |
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| 19 |
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| 20 |
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|
| 21 |
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| 22 |
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|
| 23 |
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|
| 24 |
"recurrence": 1,
|
|
@@ -28,7 +28,7 @@
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|
| 28 |
"reward_clip": 1000.0,
|
| 29 |
"value_bootstrap": false,
|
| 30 |
"normalize_returns": true,
|
| 31 |
-
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| 32 |
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|
| 33 |
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|
| 34 |
"exploration_loss": "entropy",
|
|
@@ -42,8 +42,8 @@
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|
| 42 |
"adam_eps": 1e-05,
|
| 43 |
"adam_beta1": 0.9,
|
| 44 |
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| 48 |
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|
| 49 |
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|
|
@@ -51,7 +51,9 @@
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|
| 51 |
"obs_subtract_mean": 0.0,
|
| 52 |
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|
| 53 |
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|
| 54 |
-
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|
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|
| 55 |
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|
| 56 |
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|
| 57 |
"actor_worker_gpus": [],
|
|
@@ -63,14 +65,14 @@
|
|
| 63 |
"flush_summaries_interval": 30,
|
| 64 |
"stats_avg": 100,
|
| 65 |
"summaries_use_frameskip": true,
|
| 66 |
-
"heartbeat_interval":
|
| 67 |
-
"heartbeat_reporting_interval":
|
| 68 |
-
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|
| 69 |
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|
| 70 |
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|
| 71 |
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|
| 72 |
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|
| 73 |
-
"save_milestones_sec":
|
| 74 |
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|
| 75 |
"save_best_metric": "reward",
|
| 76 |
"save_best_after": 100000,
|
|
@@ -104,7 +106,7 @@
|
|
| 104 |
"use_record_episode_statistics": true,
|
| 105 |
"with_wandb": true,
|
| 106 |
"wandb_user": "matt-stammers",
|
| 107 |
-
"wandb_project": "
|
| 108 |
"wandb_group": "atari_gopher",
|
| 109 |
"wandb_job_type": "SF",
|
| 110 |
"wandb_tags": [
|
|
@@ -122,16 +124,32 @@
|
|
| 122 |
"pbt_target_objective": "true_objective",
|
| 123 |
"pbt_perturb_min": 1.1,
|
| 124 |
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|
| 125 |
-
"command_line": "--algo=APPO --env=atari_gopher --experiment=
|
| 126 |
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|
| 127 |
"algo": "APPO",
|
| 128 |
"env": "atari_gopher",
|
| 129 |
-
"experiment": "
|
| 130 |
"train_dir": "./train_atari",
|
|
|
|
|
|
|
| 131 |
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|
|
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|
|
| 132 |
"with_wandb": true,
|
| 133 |
"wandb_user": "matt-stammers",
|
| 134 |
-
"wandb_project": "
|
| 135 |
"wandb_group": "atari_gopher",
|
| 136 |
"wandb_job_type": "SF",
|
| 137 |
"wandb_tags": [
|
|
@@ -140,5 +158,5 @@
|
|
| 140 |
},
|
| 141 |
"git_hash": "5fff97c2f535da5987d358cdbe6927cccd43621e",
|
| 142 |
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|
| 143 |
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"wandb_unique_id": "
|
| 144 |
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|
|
|
|
| 2 |
"help": false,
|
| 3 |
"algo": "APPO",
|
| 4 |
"env": "atari_gopher",
|
| 5 |
+
"experiment": "atari_gopher_APPO",
|
| 6 |
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|
| 7 |
+
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|
| 8 |
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|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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|
| 14 |
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| 15 |
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|
| 16 |
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|
| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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|
| 21 |
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|
| 22 |
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|
| 23 |
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|
| 24 |
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|
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|
| 28 |
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|
| 29 |
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|
| 30 |
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|
| 31 |
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"exploration_loss_coeff": 0.0004677351413,
|
| 32 |
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|
| 33 |
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|
| 34 |
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|
|
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|
| 42 |
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|
| 43 |
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|
| 44 |
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|
| 45 |
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|
| 46 |
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| 47 |
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| 49 |
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| 51 |
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| 52 |
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| 53 |
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| 54 |
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| 55 |
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|
| 56 |
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| 59 |
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|
| 65 |
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| 66 |
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| 67 |
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|
| 68 |
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| 69 |
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|
| 70 |
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|
| 71 |
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| 72 |
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| 73 |
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| 74 |
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| 75 |
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|
| 76 |
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|
| 77 |
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| 78 |
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|
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|
| 106 |
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|
| 107 |
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|
| 108 |
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|
| 109 |
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"wandb_project": "atari_APPO",
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| 110 |
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|
| 111 |
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| 112 |
"wandb_tags": [
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|
|
|
| 124 |
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| 125 |
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|
| 126 |
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|
| 127 |
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"command_line": "--algo=APPO --env=atari_gopher --experiment=atari_gopher_APPO --num_policies=2 --restart_behavior=restart --train_dir=./train_atari --train_for_env_steps=100000000 --seed=1234 --num_workers=16 --num_envs_per_worker=2 --num_batches_per_epoch=8 --async_rl=true --batched_sampling=true --batch_size=1024 --max_grad_norm=0 --learning_rate=0.0003033891184 --heartbeat_interval=10 --heartbeat_reporting_interval=60 --save_milestones_sec=1200 --num_epochs=4 --exploration_loss_coeff=0.0004677351413 --with_wandb=true --wandb_user=matt-stammers --wandb_project=atari_APPO --wandb_group=atari_gopher --wandb_job_type=SF --wandb_tags=atari",
|
| 128 |
"cli_args": {
|
| 129 |
"algo": "APPO",
|
| 130 |
"env": "atari_gopher",
|
| 131 |
+
"experiment": "atari_gopher_APPO",
|
| 132 |
"train_dir": "./train_atari",
|
| 133 |
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"restart_behavior": "restart",
|
| 134 |
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"seed": 1234,
|
| 135 |
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|
| 136 |
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| 137 |
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| 138 |
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|
| 139 |
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|
| 140 |
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|
| 141 |
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|
| 142 |
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|
| 143 |
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| 144 |
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| 145 |
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| 148 |
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| 149 |
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|
| 150 |
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|
| 151 |
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| 152 |
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"wandb_project": "atari_APPO",
|
| 153 |
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| 154 |
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| 155 |
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|
| 158 |
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|
| 162 |
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git.diff
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|
| 1 |
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replay.mp4
CHANGED
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| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:20a3e661302811bce95772257e7c26e3993f93ee940255ae50ec6d488e1f0949
|
| 3 |
+
size 6061940
|
sf_log.txt
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