Upload folder using huggingface_hub

.summary/0/events.out.tfevents.1704458512.bd4e88014723

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README.md

@@ -15,7 +15,7 @@ model-index:
       type: doom_health_gathering_supreme
     metrics:
     - type: mean_reward
-      value: 9.31 +/- 4.55
+      value: 10.05 +/- 5.52
       name: mean_reward
       verified: false
 ---
@@ -38,19 +38,19 @@ python -m sample_factory.huggingface.load_from_hub -r gchindemi/appo-vizdoom_hea
 
 To run the model after download, use the `enjoy` script corresponding to this environment:
 ```
-python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=appo-vizdoom_health_gathering_supreme
+python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=appo-vizdoom_health_gathering_supreme
 ```
 
 
 You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
 See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
-        
+    
 ## Training with this model
 
 To continue training with this model, use the `train` script corresponding to this environment:
 ```
-python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=appo-vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
+python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=appo-vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
 ```
 
 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.
-        
+    

checkpoint_p0/best_000002374_9723904_reward_31.566.pth

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checkpoint_p0/checkpoint_000002361_9670656.pth

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+size 34929477

checkpoint_p0/checkpoint_000002443_10006528.pth

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config.json

@@ -46,6 +46,8 @@
   "learning_rate": 0.0001,
   "lr_schedule": "constant",
   "lr_schedule_kl_threshold": 0.008,
+  "lr_adaptive_min": 1e-06,
+  "lr_adaptive_max": 0.01,
   "obs_subtract_mean": 0.0,
   "obs_scale": 255.0,
   "normalize_input": true,
@@ -63,7 +65,7 @@
   "summaries_use_frameskip": true,
   "heartbeat_interval": 20,
   "heartbeat_reporting_interval": 600,
-  "train_for_env_steps": 4000000,
+  "train_for_env_steps": 10000000,
   "train_for_seconds": 10000000000,
   "save_every_sec": 120,
   "keep_checkpoints": 2,
@@ -128,14 +130,13 @@
   "wide_aspect_ratio": false,
   "eval_env_frameskip": 1,
   "fps": 35,
-  "command_line": "--env=doom_health_gathering_supreme --num_workers=8 --num_envs_per_worker=4 --train_for_env_steps=4000000",
+  "command_line": "--env=doom_health_gathering_supreme --num_workers=8 --num_envs_per_worker=4 --train_for_env_steps=10_000_000",
   "cli_args": {
     "env": "doom_health_gathering_supreme",
     "num_workers": 8,
     "num_envs_per_worker": 4,
-    "train_for_env_steps": 4000000
+    "train_for_env_steps": 10000000
   },
   "git_hash": "unknown",
-  "git_repo_name": "not a git repository",
-  "train_script": ".usr.local.lib.python3.10.dist-packages.colab_kernel_launcher"
+  "git_repo_name": "not a git repository"
 }

replay.mp4

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