Initial commit

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,71 @@
+---
+library_name: stable-baselines3
+tags:
+- MountainCar-v0
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: MountainCar-v0
+      type: MountainCar-v0
+    metrics:
+    - type: mean_reward
+      value: -110.70 +/- 19.61
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **MountainCar-v0**
+This is a trained model of a **A2C** agent playing **MountainCar-v0**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
+and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
+
+The RL Zoo is a training framework for Stable Baselines3
+reinforcement learning agents,
+with hyperparameter optimization and pre-trained agents included.
+
+## Usage (with SB3 RL Zoo)
+
+RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
+SB3: https://github.com/DLR-RM/stable-baselines3<br/>
+SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
+
+Install the RL Zoo (with SB3 and SB3-Contrib):
+```bash
+pip install rl_zoo3
+```
+
+```
+# Download model and save it into the logs/ folder
+python -m rl_zoo3.load_from_hub --algo a2c --env MountainCar-v0 -orga qgallouedec -f logs/
+python -m rl_zoo3.enjoy --algo a2c --env MountainCar-v0  -f logs/
+```
+
+If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
+```
+python -m rl_zoo3.load_from_hub --algo a2c --env MountainCar-v0 -orga qgallouedec -f logs/
+python -m rl_zoo3.enjoy --algo a2c --env MountainCar-v0  -f logs/
+```
+
+## Training (with the RL Zoo)
+```
+python -m rl_zoo3.train --algo a2c --env MountainCar-v0 -f logs/
+# Upload the model and generate video (when possible)
+python -m rl_zoo3.push_to_hub --algo a2c --env MountainCar-v0 -f logs/ -orga qgallouedec
+```
+
+## Hyperparameters
+```python
+OrderedDict([('ent_coef', 0.0),
+             ('n_envs', 16),
+             ('n_timesteps', 1000000.0),
+             ('normalize', True),
+             ('policy', 'MlpPolicy'),
+             ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
+```

a2c-MountainCar-v0.zip

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+oid sha256:189a4a114ecb39e74eddc591f3950b8e0c806aa697ae03438cd6fa19242cb405
+size 96193

a2c-MountainCar-v0/_stable_baselines3_version

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+1.8.0a6

a2c-MountainCar-v0/data

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+        "__module__": "stable_baselines3.common.policies",
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+        "__abstractmethods__": "frozenset()",
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a2c-MountainCar-v0/policy.optimizer.pth

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a2c-MountainCar-v0/pytorch_variables.pth

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a2c-MountainCar-v0/system_info.txt

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+- OS: Linux-5.19.0-32-generic-x86_64-with-glibc2.35 # 33~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Mon Jan 30 17:03:34 UTC 2
+- Python: 3.9.12
+- Stable-Baselines3: 1.8.0a6
+- PyTorch: 1.13.1+cu117
+- GPU Enabled: True
+- Numpy: 1.24.1
+- Gym: 0.21.0

args.yml

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+!!python/object/apply:collections.OrderedDict
+- - - algo
+    - a2c
+  - - device
+    - auto
+  - - env
+    - MountainCar-v0
+  - - env_kwargs
+    - null
+  - - eval_episodes
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+  - - eval_freq
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+  - - gym_packages
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+  - - hyperparams
+    - null
+  - - log_folder
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+  - - max_total_trials
+    - null
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+    - 1
+  - - n_evaluations
+    - null
+  - - n_jobs
+    - 1
+  - - n_startup_trials
+    - 10
+  - - n_timesteps
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+  - - n_trials
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+  - - no_optim_plots
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+  - - optimization_log_path
+    - null
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+  - - pruner
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+  - - seed
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+  - - study_name
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+  - - track
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+  - - trained_agent
+    - ''
+  - - truncate_last_trajectory
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+  - - uuid
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+  - - vec_env
+    - dummy
+  - - verbose
+    - 1
+  - - wandb_entity
+    - openrlbenchmark
+  - - wandb_project_name
+    - sb3
+  - - yaml_file
+    - null

config.yml

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+!!python/object/apply:collections.OrderedDict
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+    - 0.0
+  - - n_envs
+    - 16
+  - - n_timesteps
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+  - - normalize
+    - true
+  - - policy
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env_kwargs.yml

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replay.mp4

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results.json

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train_eval_metrics.zip

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