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.gitattributes CHANGED
@@ -25,3 +25,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zstandard filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zstandard filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ *.mp4 filter=lfs diff=lfs merge=lfs -text
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+ vec_normalize.pkl filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,63 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ library_name: stable-baselines3
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+ tags:
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+ - Ant-v3
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+ - deep-reinforcement-learning
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+ - reinforcement-learning
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+ - stable-baselines3
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+ model-index:
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+ - name: ARS
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+ results:
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+ - metrics:
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+ - type: mean_reward
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+ value: 2318.51 +/- 13.05
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+ name: mean_reward
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+ task:
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+ type: reinforcement-learning
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+ name: reinforcement-learning
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+ dataset:
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+ name: Ant-v3
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+ type: Ant-v3
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+ ---
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+
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+ # **ARS** Agent playing **Ant-v3**
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+ This is a trained model of a **ARS** agent playing **Ant-v3**
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+ using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
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+ and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
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+
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+ The RL Zoo is a training framework for Stable Baselines3
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+ reinforcement learning agents,
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+ with hyperparameter optimization and pre-trained agents included.
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+
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+ ## Usage (with SB3 RL Zoo)
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+
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+ RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
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+ SB3: https://github.com/DLR-RM/stable-baselines3<br/>
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+ SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
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+
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+ ```
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+ # Download model and save it into the logs/ folder
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+ python -m utils.load_from_hub --algo ars --env Ant-v3 -orga sb3 -f logs/
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+ python enjoy.py --algo ars --env Ant-v3 -f logs/
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+ ```
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+
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+ ## Training (with the RL Zoo)
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+ ```
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+ python train.py --algo ars --env Ant-v3 -f logs/
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+ # Upload the model and generate video (when possible)
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+ python -m utils.push_to_hub --algo ars --env Ant-v3 -f logs/ -orga sb3
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+ ```
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+
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+ ## Hyperparameters
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+ ```python
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+ OrderedDict([('alive_bonus_offset', -1),
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+ ('delta_std', 0.025),
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+ ('learning_rate', 0.015),
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+ ('n_delta', 60),
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+ ('n_envs', 16),
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+ ('n_timesteps', 75000000.0),
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+ ('n_top', 20),
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+ ('normalize', 'dict(norm_obs=True, norm_reward=False)'),
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+ ('policy', 'LinearPolicy'),
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+ ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
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+ ```
args.yml ADDED
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+ !!python/object/apply:collections.OrderedDict
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+ - - - algo
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+ - ars
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+ - - env
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+ - Ant-v3
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+ - - env_kwargs
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+ - null
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+ - - eval_episodes
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+ - 20
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+ - - eval_freq
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+ - 100000
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+ - - gym_packages
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+ - []
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+ - - hyperparams
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+ - n_envs: 16
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+ - - log_folder
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+ - rl-trained-agents/
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+ - - log_interval
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+ - 10
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+ - - n_eval_envs
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+ - 5
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+ - - n_evaluations
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+ - 20
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+ - - n_jobs
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+ - 1
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+ - - n_startup_trials
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+ - 10
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+ - - n_timesteps
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+ - 10000000
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+ - - n_trials
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+ - 10
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+ - - no_optim_plots
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+ - false
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+ - - num_threads
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+ - -1
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+ - - optimization_log_path
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+ - null
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+ - - optimize_hyperparameters
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+ - false
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+ - - pruner
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+ - median
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+ - - sampler
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+ - tpe
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+ - - save_freq
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+ - -1
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+ - - save_replay_buffer
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+ - false
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+ - - seed
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+ - 3734959199
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+ - - storage
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+ - null
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+ - - study_name
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+ - null
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+ - - tensorboard_log
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+ - ''
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+ - - trained_agent
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+ - ''
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+ - - truncate_last_trajectory
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+ - true
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+ - - uuid
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+ - false
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+ - - vec_env
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+ - dummy
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+ - - verbose
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+ - 1
ars-Ant-v3.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:9992cb04be614655b87bac323c984b690a1e7645801626d618843b33c447c23d
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+ size 37093
ars-Ant-v3/_stable_baselines3_version ADDED
@@ -0,0 +1 @@
 
 
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+ 1.5.1a8
ars-Ant-v3/data ADDED
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+ {
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+ "policy_class": {
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+ ":type:": "<class 'abc.ABCMeta'>",
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+ ":serialized:": "gASVMAAAAAAAAACMGHNiM19jb250cmliLmFycy5wb2xpY2llc5SMD0FSU0xpbmVhclBvbGljeZSTlC4=",
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+ "__module__": "sb3_contrib.ars.policies",
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+ "__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 ",
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+ "__init__": "<function ARSLinearPolicy.__init__ at 0x7fd2079db7a0>",
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+ "__abstractmethods__": "frozenset()",
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+ "_abc_impl": "<_abc_data object at 0x7fd207c9c870>"
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+ },
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+ "verbose": 1,
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+ "policy_kwargs": {},
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+ "observation_space": {
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+ ":type:": "<class 'gym.spaces.box.Box'>",
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+ "bounded_below": "[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 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 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 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 False False False False False False False False\n False False False]",
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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 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 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 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 False False False False False False False False\n False False False]",
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+ "_np_random": null,
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+ "_shape": [
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+ 111
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+ ]
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+ },
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+ "action_space": {
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