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 +1,77 @@
+---
+library_name: stable-baselines3
+tags:
+- AntBulletEnv-v0
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: ARS
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: AntBulletEnv-v0
+      type: AntBulletEnv-v0
+    metrics:
+    - type: mean_reward
+      value: 979.19 +/- 4.29
+      name: mean_reward
+      verified: false
+---
+
+# **ARS** Agent playing **AntBulletEnv-v0**
+This is a trained model of a **ARS** agent playing **AntBulletEnv-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 ars --env AntBulletEnv-v0 -orga qgallouedec -f logs/
+python -m rl_zoo3.enjoy --algo ars --env AntBulletEnv-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 ars --env AntBulletEnv-v0 -orga qgallouedec -f logs/
+python -m rl_zoo3.enjoy --algo ars --env AntBulletEnv-v0  -f logs/
+```
+
+## Training (with the RL Zoo)
+```
+python -m rl_zoo3.train --algo ars --env AntBulletEnv-v0 -f logs/
+# Upload the model and generate video (when possible)
+python -m rl_zoo3.push_to_hub --algo ars --env AntBulletEnv-v0 -f logs/ -orga qgallouedec
+```
+
+## Hyperparameters
+```python
+OrderedDict([('alive_bonus_offset', 0),
+             ('delta_std', 0.03),
+             ('learning_rate', 0.02),
+             ('n_delta', 32),
+             ('n_envs', 1),
+             ('n_timesteps', 75000000.0),
+             ('n_top', 32),
+             ('normalize', 'dict(norm_obs=True, norm_reward=False)'),
+             ('policy', 'MlpPolicy'),
+             ('policy_kwargs', 'dict(net_arch=[128, 64])'),
+             ('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
+    - AntBulletEnv-v0
+  - - 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
+    - 729443604
+  - - storage
+    - null
+  - - study_name
+    - null
+  - - tensorboard_log
+    - runs/AntBulletEnv-v0__ars__729443604__1671039850
+  - - 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-AntBulletEnv-v0.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:76a4a542403662387f9e845ac306e894624e5ce2a91dfa855055143554d50cc1
+size 137970

ars-AntBulletEnv-v0/_stable_baselines3_version

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+1.8.0a6

ars-AntBulletEnv-v0/data

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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 0x7f5bb809a280>",
+        "_get_constructor_parameters": "<function ARSPolicy._get_constructor_parameters at 0x7f5bb809a310>",
+        "forward": "<function ARSPolicy.forward at 0x7f5bb809a3a0>",
+        "_predict": "<function ARSPolicy._predict at 0x7f5bb809a430>",
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+        "_abc_impl": "<_abc._abc_data object at 0x7f5bb8099ac0>"
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+        "bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]",
+        "_np_random": null
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+    },
+    "processes": null,
+    "old_count": 0,
+    "n_params": 12488
+}

ars-AntBulletEnv-v0/policy.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e6e99a761f525723a4d2a68b93bf43a1834dc18c76ed024ce5b1f756bd67477e
+size 52055

ars-AntBulletEnv-v0/pytorch_variables.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

ars-AntBulletEnv-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

config.yml

@@ -0,0 +1,23 @@
+!!python/object/apply:collections.OrderedDict
+- - - alive_bonus_offset
+    - 0
+  - - delta_std
+    - 0.03
+  - - learning_rate
+    - 0.02
+  - - n_delta
+    - 32
+  - - n_envs
+    - 1
+  - - n_timesteps
+    - 75000000.0
+  - - n_top
+    - 32
+  - - normalize
+    - dict(norm_obs=True, norm_reward=False)
+  - - policy
+    - MlpPolicy
+  - - policy_kwargs
+    - dict(net_arch=[128, 64])
+  - - zero_policy
+    - false

env_kwargs.yml

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+{}

replay.mp4

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+version https://git-lfs.github.com/spec/v1
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+size 240598

results.json

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train_eval_metrics.zip

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vec_normalize.pkl

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