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 +1,71 @@
+---
+library_name: stable-baselines3
+tags:
+- Hopper-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 2410.11 +/- 9.86
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: Hopper-v3
+      type: Hopper-v3
+---
+
+# **PPO** Agent playing **Hopper-v3**
+This is a trained model of a **PPO** agent playing **Hopper-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 ppo --env Hopper-v3 -orga sb3 -f logs/
+python enjoy.py --algo ppo --env Hopper-v3  -f logs/
+```
+
+## Training (with the RL Zoo)
+```
+python train.py --algo ppo --env Hopper-v3 -f logs/
+# Upload the model and generate video (when possible)
+python -m utils.push_to_hub --algo ppo --env Hopper-v3 -f logs/ -orga sb3
+```
+
+## Hyperparameters
+```python
+OrderedDict([('batch_size', 32),
+             ('clip_range', 0.2),
+             ('ent_coef', 0.00229519),
+             ('gae_lambda', 0.99),
+             ('gamma', 0.999),
+             ('learning_rate', 9.80828e-05),
+             ('max_grad_norm', 0.7),
+             ('n_envs', 1),
+             ('n_epochs', 5),
+             ('n_steps', 512),
+             ('n_timesteps', 1000000.0),
+             ('normalize', True),
+             ('policy', 'MlpPolicy'),
+             ('policy_kwargs',
+              'dict( log_std_init=-2, ortho_init=False, activation_fn=nn.ReLU, '
+              'net_arch=[dict(pi=[256, 256], vf=[256, 256])] )'),
+             ('vf_coef', 0.835671),
+             ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
+```

args.yml

@@ -0,0 +1,65 @@
+!!python/object/apply:collections.OrderedDict
+- - - algo
+    - ppo
+  - - env
+    - Hopper-v3
+  - - env_kwargs
+    - null
+  - - eval_episodes
+    - 20
+  - - eval_freq
+    - 25000
+  - - gym_packages
+    - []
+  - - hyperparams
+    - null
+  - - log_folder
+    - logs/
+  - - log_interval
+    - 10
+  - - 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
+    - 2
+  - - optimization_log_path
+    - null
+  - - optimize_hyperparameters
+    - false
+  - - pruner
+    - median
+  - - sampler
+    - tpe
+  - - save_freq
+    - -1
+  - - save_replay_buffer
+    - false
+  - - seed
+    - 594371
+  - - storage
+    - null
+  - - study_name
+    - null
+  - - tensorboard_log
+    - ''
+  - - trained_agent
+    - ''
+  - - truncate_last_trajectory
+    - true
+  - - uuid
+    - true
+  - - vec_env
+    - dummy
+  - - verbose
+    - 1

config.yml

@@ -0,0 +1,32 @@
+!!python/object/apply:collections.OrderedDict
+- - - batch_size
+    - 32
+  - - clip_range
+    - 0.2
+  - - ent_coef
+    - 0.00229519
+  - - gae_lambda
+    - 0.99
+  - - gamma
+    - 0.999
+  - - learning_rate
+    - 9.80828e-05
+  - - max_grad_norm
+    - 0.7
+  - - n_envs
+    - 1
+  - - n_epochs
+    - 5
+  - - n_steps
+    - 512
+  - - n_timesteps
+    - 1000000.0
+  - - normalize
+    - true
+  - - policy
+    - MlpPolicy
+  - - policy_kwargs
+    - dict( log_std_init=-2, ortho_init=False, activation_fn=nn.ReLU, net_arch=[dict(pi=[256,
+      256], vf=[256, 256])] )
+  - - vf_coef
+    - 0.835671

env_kwargs.yml

@@ -0,0 +1 @@
+{}

ppo-Hopper-v3.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f539b54ee10a23afb1f32e9042de3860d38d2f9d5437b0518ce0e53c5b8813f7
+size 1698284

ppo-Hopper-v3/_stable_baselines3_version

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

ppo-Hopper-v3/data

@@ -0,0 +1,120 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        "__module__": "stable_baselines3.common.policies",
+        "__doc__": "\n    Policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param ortho_init: Whether to use or not orthogonal initialization\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f6161d4b950>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6161d4b9e0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6161d4ba70>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6161d4bb00>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f6161d4bb90>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f6161d4bc20>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6161d4bcb0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f6161d4bd40>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6161d4bdd0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6161d4be60>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6161d4bef0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f6161d9d840>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gASVjgAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA1hY3RpdmF0aW9uX2ZulIwbdG9yY2gubm4ubW9kdWxlcy5hY3RpdmF0aW9ulIwEUmVMVZSTlIwIbmV0X2FyY2iUXZR9lCiMAnBplF2UKE0AAU0AAWWMAnZmlF2UKE0AAU0AAWV1YXUu",
+        "log_std_init": -2,
+        "ortho_init": false,
+        "activation_fn": "<class 'torch.nn.modules.activation.ReLU'>",
+        "net_arch": [
+            {
+                "pi": [
+                    256,
+                    256
+                ],
+                "vf": [
+                    256,
+                    256
+                ]
+            }
+        ]
+    },
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float64",
+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf inf inf inf]",
+        "bounded_below": "[False False False False False False False False False False False]",
+        "bounded_above": "[False False False False False False False False False False False]",
+        "_np_random": null,
+        "_shape": [
+            11
+        ]
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "low": "[-1. -1. -1.]",
+        "high": "[1. 1. 1.]",
+        "bounded_below": "[ True  True  True]",
+        "bounded_above": "[ True  True  True]",
+        "_np_random": "RandomState(MT19937)",
+        "_shape": [
+            3
+        ]
+    },
+    "n_envs": 1,
+    "num_timesteps": 1000448,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": 0,
+    "action_noise": null,
+    "start_time": 1637082108.2294784,
+    "learning_rate": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": null,
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gASViQAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwGFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDAQCUdJRiLg=="
+    },
+    "_last_original_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gASV4gAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwFLC4aUaAOMBWR0eXBllJOUjAJmOJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiiUNYh2jyPgQH9D9wlxwhrzE/v55pbDHx+WQ/b6X7OZYdcz/STfH7cJ9ZvyS/GIJ91Vo/ouA4FIFoZz+ASbtkqpRWvxT5KXiO81i/QMaKFAoKJj/SxzsFaEhav5R0lGIu"
+    },
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.00044800000000000395,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 9770,
+    "n_steps": 512,
+    "gamma": 0.999,
+    "gae_lambda": 0.99,
+    "ent_coef": 0.00229519,
+    "vf_coef": 0.835671,
+    "max_grad_norm": 0.7,
+    "batch_size": 32,
+    "n_epochs": 5,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

ppo-Hopper-v3/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cf1253728263042b6ec6253a36a93033789639a157d09be80e7681b209daf913
+size 1117143

ppo-Hopper-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:29aa4edba2f3111654e74570acff7452c5964c7ad195151c9ac2408501eb4ee9
+size 559422

ppo-Hopper-v3/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

ppo-Hopper-v3/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.13.0-44-generic-x86_64-with-debian-bullseye-sid #49~20.04.1-Ubuntu SMP Wed May 18 18:44:28 UTC 2022
+Python: 3.7.10
+Stable-Baselines3: 1.5.1a8
+PyTorch: 1.11.0
+GPU Enabled: True
+Numpy: 1.21.2
+Gym: 0.21.0

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fbeb68f2c7e3e1a5079c34c904b9beb379c5d2fa32d23471e0052ef03a4da5b0
+size 1578739

results.json

@@ -0,0 +1 @@
+{"mean_reward": 2410.1057789, "std_reward": 9.859325548491421, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-06-02T13:59:41.637518"}

train_eval_metrics.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:78b59e3ffd29d285d89a27c7aefb9887bf2202d377fd4c63edd398ffa3479810
+size 292709

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a17eacce0951306abe743d960b9c01b5027773c40a9faa11a36575903ab4c532
+size 4730