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 +1,63 @@
+---
+library_name: stable-baselines3
+tags:
+- HalfCheetah-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: ARS
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 4046.14 +/- 2253.39
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: HalfCheetah-v3
+      type: HalfCheetah-v3
+---
+
+# **ARS** Agent playing **HalfCheetah-v3**
+This is a trained model of a **ARS** agent playing **HalfCheetah-v3**
+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 HalfCheetah-v3 -orga sb3 -f logs/
+python enjoy.py --algo ars --env HalfCheetah-v3  -f logs/
+```
+
+## Training (with the RL Zoo)
+```
+python train.py --algo ars --env HalfCheetah-v3 -f logs/
+# Upload the model and generate video (when possible)
+python -m utils.push_to_hub --algo ars --env HalfCheetah-v3 -f logs/ -orga sb3
+```
+
+## Hyperparameters
+```python
+OrderedDict([('alive_bonus_offset', 0),
+             ('delta_std', 0.03),
+             ('learning_rate', 0.02),
+             ('n_delta', 32),
+             ('n_envs', 16),
+             ('n_timesteps', 12500000.0),
+             ('n_top', 4),
+             ('normalize', 'dict(norm_obs=True, norm_reward=False)'),
+             ('policy', 'LinearPolicy'),
+             ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
+```

args.yml

@@ -0,0 +1,65 @@
+!!python/object/apply:collections.OrderedDict
+- - - algo
+    - ars
+  - - env
+    - HalfCheetah-v3
+  - - env_kwargs
+    - null
+  - - eval_episodes
+    - 20
+  - - eval_freq
+    - 100000
+  - - gym_packages
+    - []
+  - - hyperparams
+    - n_envs: 16
+  - - log_folder
+    - rl-trained-agents/
+  - - log_interval
+    - 10
+  - - n_eval_envs
+    - 5
+  - - n_evaluations
+    - 20
+  - - n_jobs
+    - 1
+  - - n_startup_trials
+    - 10
+  - - n_timesteps
+    - 10000000
+  - - 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
+    - 3405556843
+  - - storage
+    - null
+  - - study_name
+    - null
+  - - tensorboard_log
+    - ''
+  - - trained_agent
+    - ''
+  - - truncate_last_trajectory
+    - true
+  - - uuid
+    - false
+  - - vec_env
+    - dummy
+  - - verbose
+    - 1

ars-HalfCheetah-v3.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f78b54fc61db6b518a72454b090a79c9623755651f7363a887cdb50879b4ee5a
+size 20352

ars-HalfCheetah-v3/_stable_baselines3_version

@@ -0,0 +1 @@
+1.5.1a8

ars-HalfCheetah-v3/data

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+        "__module__": "sb3_contrib.ars.policies",
+        "__doc__": "\n    Linear 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 with_bias: With or without bias on the output\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 ARSLinearPolicy.__init__ at 0x7fb71f1a67a0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fb71f468870>"
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+        "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf]",
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+        "bounded_above": "[False False False False False False False False False False False False\n False False False False False]",
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