Pushing the 2nd version of Lunar lander RL model to HF Hub

Lunar_Lander_VSrinivas.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:84b45a58b02cf80f60c83774159d0ffb7e3b28e374618a0ad1ab019b62123716
+size 150054

Lunar_Lander_VSrinivas/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0a5

Lunar_Lander_VSrinivas/data

@@ -0,0 +1,99 @@
+{
+    "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 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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 0x7a7e451fb6d0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7a7e451fb760>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7a7e451fb7f0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7a7e451fb880>",
+        "_build": "<function ActorCriticPolicy._build at 0x7a7e451fb910>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7a7e451fb9a0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7a7e451fba30>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7a7e451fbac0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7a7e451fbb50>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7a7e451fbbe0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7a7e451fbc70>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7a7e451fbd00>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7a7e451a3940>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1702467074916607402,
+    "learning_rate": 0.0005,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVswAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJZAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiS0CFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.015808000000000044,
+    "_stats_window_size": 100,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 992,
+    "observation_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True]",
+        "_shape": [
+            8
+        ],
+        "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 64,
+    "n_steps": 256,
+    "gamma": 0.999,
+    "gae_lambda": 0.98,
+    "ent_coef": 0.01,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 32,
+    "n_epochs": 16,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    }
+}

Lunar_Lander_VSrinivas/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:18cefb82abfa6104439f19dadca5de81dc5a52f327960e27c70dab0c3962986a
+size 88362

Lunar_Lander_VSrinivas/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9a51caa81b383cb952ad071d6c31900b74a8dad460f4fc0b92c0d6564cdc423e
+size 43762

Lunar_Lander_VSrinivas/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
+size 864

Lunar_Lander_VSrinivas/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.1.0+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

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: 252.72 +/- 40.43
+      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 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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 0x7a7e451fb6d0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7a7e451fb760>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7a7e451fb7f0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7a7e451fb880>", "_build": "<function ActorCriticPolicy._build at 0x7a7e451fb910>", "forward": "<function ActorCriticPolicy.forward at 0x7a7e451fb9a0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7a7e451fba30>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7a7e451fbac0>", "_predict": "<function ActorCriticPolicy._predict at 0x7a7e451fbb50>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7a7e451fbbe0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7a7e451fbc70>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7a7e451fbd00>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7a7e451a3940>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1702467074916607402, "learning_rate": 0.0005, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVswAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJZAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiS0CFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 992, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_shape": [8], "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 64, "n_steps": 256, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 32, "n_epochs": 16, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.1.0+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 252.72087281140716, "std_reward": 40.433827387752736, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-12-13T12:21:28.282436"}