Initial commit

.gitattributes

@@ -25,3 +25,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+vec_normalize.pkl filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,64 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLanderContinuous-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: ARS
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 207.05 +/- 114.40
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLanderContinuous-v2
+      type: LunarLanderContinuous-v2
+---
+
+# **ARS** Agent playing **LunarLanderContinuous-v2**
+This is a trained model of a **ARS** agent playing **LunarLanderContinuous-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
+
+```
+# Download model and save it into the logs/ folder
+python -m utils.load_from_hub --algo ars --env LunarLanderContinuous-v2 -orga sb3 -f logs/
+python enjoy.py --algo ars --env LunarLanderContinuous-v2  -f logs/
+```
+
+## Training (with the RL Zoo)
+```
+python train.py --algo ars --env LunarLanderContinuous-v2 -f logs/
+# Upload the model and generate video (when possible)
+python -m utils.push_to_hub --algo ars --env LunarLanderContinuous-v2 -f logs/ -orga sb3
+```
+
+## Hyperparameters
+```python
+OrderedDict([('delta_std', 0.1),
+             ('learning_rate', 0.018),
+             ('n_delta', 4),
+             ('n_envs', 8),
+             ('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 +1,65 @@
+!!python/object/apply:collections.OrderedDict
+- - - algo
+    - ars
+  - - env
+    - LunarLanderContinuous-v2
+  - - env_kwargs
+    - null
+  - - eval_episodes
+    - 20
+  - - eval_freq
+    - 100000
+  - - gym_packages
+    - []
+  - - hyperparams
+    - n_envs: 8
+  - - log_folder
+    - rl-trained-agents/
+  - - log_interval
+    - 20
+  - - n_eval_envs
+    - 5
+  - - n_evaluations
+    - 20
+  - - n_jobs
+    - 1
+  - - n_startup_trials
+    - 10
+  - - n_timesteps
+    - -1
+  - - n_trials
+    - 10
+  - - no_optim_plots
+    - false
+  - - num_threads
+    - -1
+  - - optimization_log_path
+    - null
+  - - optimize_hyperparameters
+    - false
+  - - pruner
+    - median
+  - - sampler
+    - tpe
+  - - save_freq
+    - -1
+  - - save_replay_buffer
+    - false
+  - - seed
+    - 388533173
+  - - storage
+    - null
+  - - study_name
+    - null
+  - - tensorboard_log
+    - ''
+  - - trained_agent
+    - ''
+  - - truncate_last_trajectory
+    - true
+  - - uuid
+    - false
+  - - vec_env
+    - dummy
+  - - verbose
+    - 1

ars-LunarLanderContinuous-v2.zip

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+oid sha256:094d6d019a76dee7248c31f8c792b3cffddb5e8b9c62667449d58aa400c5a5ba
+size 17336

ars-LunarLanderContinuous-v2/_stable_baselines3_version

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+1.5.1a8

ars-LunarLanderContinuous-v2/data

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+{
+    "policy_class": {
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+        ":serialized:": "gASVKgAAAAAAAACMGHNiM19jb250cmliLmFycy5wb2xpY2llc5SMCUFSU1BvbGljeZSTlC4=",
+        "__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 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 0x7f8176767e60>",
+        "_get_constructor_parameters": "<function ARSPolicy._get_constructor_parameters at 0x7f817676f5f0>",
+        "forward": "<function ARSPolicy.forward at 0x7f817676f680>",
+        "_predict": "<function ARSPolicy._predict at 0x7f817676f710>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f8176a31840>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
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+            16
+        ]
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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]",
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+Python: 3.7.10
+Stable-Baselines3: 1.5.1a8
+PyTorch: 1.11.0
+GPU Enabled: True
+Numpy: 1.21.2
+Gym: 0.21.0

config.yml

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+!!python/object/apply:collections.OrderedDict
+- - - delta_std
+    - 0.1
+  - - learning_rate
+    - 0.018
+  - - n_delta
+    - 4
+  - - n_envs
+    - 8
+  - - 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

env_kwargs.yml

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replay.mp4

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