Uploading model to HuggingFace

Lander_1mSteps.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:555075c227b657a01584ce6bc18e08a2478e70686a4c929f91e0cd2da440e5fb
+size 147089

Lander_1mSteps/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.2

Lander_1mSteps/data

@@ -0,0 +1,94 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        "__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 0x7fb896911820>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fb8969118b0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fb896911940>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fb8969119d0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fb896911a60>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fb896911af0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fb896911b80>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fb896911c10>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fb896911ca0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fb896911d30>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fb896911dc0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fb896907fc0>"
+    },
+    "verbose": 0,
+    "policy_kwargs": {},
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            8
+        ],
+        "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]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": 4,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "num_timesteps": 1507328,
+    "_total_timesteps": 1500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1671208261994945999,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.004885333333333408,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 460,
+    "n_steps": 2048,
+    "gamma": 0.99,
+    "gae_lambda": 0.95,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 10,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

Lander_1mSteps/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:34e0b38967a777183b29065ac97337a83a81e0a3c0cf8d3f0a857263a79c34ce
+size 87929

Lander_1mSteps/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:419ba2e3edb89aa1bc372823bff3e95b48a317746eb7ecac93d2563e54a8a430
+size 43201

Lander_1mSteps/pytorch_variables.pth

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

Lander_1mSteps/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
+Python: 3.8.16
+Stable-Baselines3: 1.6.2
+PyTorch: 1.13.0+cu116
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 270.37 +/- 20.86
+      name: mean_reward
+      verified: false
+---
+
+# **PPO** Agent playing **LunarLander-v2**
+This is a trained model of a **PPO** agent playing **LunarLander-v2**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+
+## Usage (with Stable-baselines3)
+TODO: Add your code
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 0x7fb896911820>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fb8969118b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fb896911940>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fb8969119d0>", "_build": "<function ActorCriticPolicy._build at 0x7fb896911a60>", "forward": "<function ActorCriticPolicy.forward at 0x7fb896911af0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fb896911b80>", "_predict": "<function ActorCriticPolicy._predict at 0x7fb896911c10>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fb896911ca0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fb896911d30>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fb896911dc0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fb896907fc0>"}, "verbose": 0, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "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]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 1507328, "_total_timesteps": 1500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1671208261994945999, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAM0EEj1SHry7a4q0vTur3LwftfQ8cB/GPAAAgD8AAIA/Zg/mPK4zibomBTE3UxAKMnaRBrvGt022AACAPwAAgD9mm1U9d3O8Pw6mWz6Me1e+MwpePQqKKDwAAAAAAAAAAKalxL0FMKe7jp6bO/9k9jvTAO28PsriPAAAgD8AAIA/AHgEu+hxsD99HvG78KGKvp4IGLzxyDS9AAAAAAAAAACAl9Y9thQOvCaoC76xABU93+F2Pbse8r0AAAAAAACAPxpUsD0y65o+A9NOvp1Wzr4IgKQ7oHbbvQAAAAAAAAAAuiITvoNKDD1GDR64cSs6vuYGVL0G4248AAAAAAAAAADNw7Q9bSpuPnAh172mfc6+vRuFPTiE+DwAAAAAAAAAABocej1BT7M/GYEaPr4BBb8JVl49dNk4PQAAAAAAAAAAM9UGPTYOqT+YKNo92+oCv5KHkDxe6hk9AAAAAAAAAACzloE+3ktZP6HlgT3ujiu/j1a1PoHUS70AAAAAAAAAAAAIJruGcrg/WnTOvH/qlj3MY7s65Ii9vAAAAAAAAAAAmljPPTqGmj+lVhc/grg8v6OBxT3SH4k+AAAAAAAAAACNWqY+t+I6P9eLJT30Jxa/MPb4PkAIXr4AAAAAAAAAAGBBXD6R1Gg+hnaIvrEhob5xy4E8kwGwvQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.004885333333333408, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 460, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 270.3676595591695, "std_reward": 20.86073822693475, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-16T17:06:12.265061"}