07.24.2024_18:42:18_BLOrigEnv_1/best_model_curr_step_1 Commit: 2a1117cccb556e694db1fb207a67de8ebb4d2e69/ Branch: b'main\n'

README.md

@@ -1,37 +1,37 @@
----
-library_name: stable-baselines3
-tags:
-- KimHerEnv
-- deep-reinforcement-learning
-- reinforcement-learning
-- stable-baselines3
-model-index:
-- name: HER
-  results:
-  - task:
-      type: reinforcement-learning
-      name: reinforcement-learning
-    dataset:
-      name: KimHerEnv
-      type: KimHerEnv
-    metrics:
-    - type: mean_reward
-      value: -5.00 +/- 0.00
-      name: mean_reward
-      verified: false
----
-
-# **HER** Agent playing **KimHerEnv**
-This is a trained model of a **HER** agent playing **KimHerEnv**
-using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
-
-## Usage (with Stable-baselines3)
-TODO: Add your code
-
-
-```python
-from stable_baselines3 import ...
-from huggingface_sb3 import load_from_hub
-
-...
-```
+---
+library_name: stable-baselines3
+tags:
+- BLAdapEnv
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: HER
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: BLAdapEnv
+      type: BLAdapEnv
+    metrics:
+    - type: mean_reward
+      value: -6.00 +/- 0.00
+      name: mean_reward
+      verified: false
+---
+
+# **HER** Agent playing **BLAdapEnv**
+This is a trained model of a **HER** agent playing **BLAdapEnv**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+
+## Usage (with Stable-baselines3)
+TODO: Add your code
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

config.json

@@ -1 +1 @@
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model/policy.pth

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model/pytorch_variables.pth

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model/system_info.txt

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+- OS: Windows-10-10.0.19045-SP0 10.0.19045
+- Python: 3.9.13
+- Stable-Baselines3: 2.3.2
+- PyTorch: 2.3.0+cpu
+- GPU Enabled: False
+- Numpy: 1.26.3
+- Cloudpickle: 3.0.0
+- Gymnasium: 0.29.1
+- OpenAI Gym: 0.26.2

results.json

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-{"mean_reward": -5.0, "std_reward": 0.0, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-07-06T13:27:55.936198"}
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