Initial commit

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.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 +1,76 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: ARS
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 62.56 +/- 108.79
+      name: mean_reward
+      verified: false
+---
+
+# **ARS** Agent playing **LunarLander-v2**
+This is a trained model of a **ARS** agent playing **LunarLander-v2**
+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 ars --env LunarLander-v2 -orga qgallouedec -f logs/
+python -m rl_zoo3.enjoy --algo ars --env LunarLander-v2  -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 ars --env LunarLander-v2 -orga qgallouedec -f logs/
+python -m rl_zoo3.enjoy --algo ars --env LunarLander-v2  -f logs/
+```
+
+## Training (with the RL Zoo)
+```
+python -m rl_zoo3.train --algo ars --env LunarLander-v2 -f logs/
+# Upload the model and generate video (when possible)
+python -m rl_zoo3.push_to_hub --algo ars --env LunarLander-v2 -f logs/ -orga qgallouedec
+```
+
+## Hyperparameters
+```python
+OrderedDict([('delta_std', 0.1),
+             ('learning_rate', 0.018),
+             ('n_delta', 6),
+             ('n_envs', 1),
+             ('n_timesteps', 2000000.0),
+             ('n_top', 1),
+             ('normalize', 'dict(norm_obs=True, norm_reward=False)'),
+             ('policy', 'MlpPolicy'),
+             ('policy_kwargs', 'dict(net_arch=[16])'),
+             ('zero_policy', False),
+             ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
+```

args.yml

@@ -0,0 +1,79 @@
+!!python/object/apply:collections.OrderedDict
+- - - algo
+    - ars
+  - - device
+    - auto
+  - - env
+    - LunarLander-v2
+  - - env_kwargs
+    - null
+  - - eval_episodes
+    - 5
+  - - eval_freq
+    - 25000
+  - - gym_packages
+    - []
+  - - hyperparams
+    - null
+  - - log_folder
+    - logs
+  - - log_interval
+    - -1
+  - - max_total_trials
+    - null
+  - - n_eval_envs
+    - 1
+  - - n_evaluations
+    - null
+  - - n_jobs
+    - 1
+  - - n_startup_trials
+    - 10
+  - - n_timesteps
+    - -1
+  - - n_trials
+    - 500
+  - - no_optim_plots
+    - false
+  - - num_threads
+    - -1
+  - - optimization_log_path
+    - null
+  - - optimize_hyperparameters
+    - false
+  - - progress
+    - false
+  - - pruner
+    - median
+  - - sampler
+    - tpe
+  - - save_freq
+    - -1
+  - - save_replay_buffer
+    - false
+  - - seed
+    - 3325122407
+  - - storage
+    - null
+  - - study_name
+    - null
+  - - tensorboard_log
+    - runs/LunarLander-v2__ars__3325122407__1671640801
+  - - track
+    - true
+  - - trained_agent
+    - ''
+  - - truncate_last_trajectory
+    - true
+  - - uuid
+    - false
+  - - vec_env
+    - dummy
+  - - verbose
+    - 1
+  - - wandb_entity
+    - openrlbenchmark
+  - - wandb_project_name
+    - sb3
+  - - yaml_file
+    - null

ars-LunarLander-v2.zip

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+oid sha256:1d1d5e454cae16e099f8e9189fd046f28e21b517f5885d2434a6431e1d5aa760
+size 17721

ars-LunarLander-v2/_stable_baselines3_version

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+1.8.0a6

ars-LunarLander-v2/data

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+{
+    "policy_class": {
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+        "__module__": "sb3_contrib.ars.policies",
+        "__doc__": "\n    Policy network for ARS.\n\n    :param observation_space: The observation space of the environment\n    :param action_space: The action space of the environment\n    :param net_arch: Network architecture, defaults to a 2 layers MLP with 64 hidden nodes.\n    :param activation_fn: Activation function\n    :param with_bias: If set to False, the layers will not learn an additive bias\n    :param squash_output: For continuous actions, whether the output is squashed\n        or not using a ``tanh()`` function. If not squashed with tanh the output will instead be clipped.\n    ",
+        "__init__": "<function ARSPolicy.__init__ at 0x7f69afb96280>",
+        "_get_constructor_parameters": "<function ARSPolicy._get_constructor_parameters at 0x7f69afb96310>",
+        "forward": "<function ARSPolicy.forward at 0x7f69afb963a0>",
+        "_predict": "<function ARSPolicy._predict at 0x7f69afb96430>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f69afb9a180>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        "net_arch": [
+            16
+        ]
+    },
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+        "_shape": [
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+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf]",
+        "bounded_below": "[False False False False False False False False]",
+        "bounded_above": "[False False False False False False False False]",
+        "_np_random": null
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+- 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

config.yml

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env_kwargs.yml

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replay.mp4

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