init

LunarLander-v2_model.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fdbd4e58ac6779fd9143f2d8a3e7a81f6daf44abe9afe58fa3dc3ebef113ddfe
+size 147242

LunarLander-v2_model/_stable_baselines3_version

@@ -0,0 +1 @@
+1.8.0

LunarLander-v2_model/data

@@ -0,0 +1,96 @@
+{
+    "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 0x7f09c09bfbe0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f09c09bfc70>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f09c09bfd00>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f09c09bfd90>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f09c09bfe20>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f09c09bfeb0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f09c09bff40>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f09c09c8040>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f09c09c80d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f09c09c8160>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f09c09c81f0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f09c09c8280>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f09c09c1fc0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 32768,
+    "_total_timesteps": 100,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1682699334841522820,
+    "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": -326.68,
+    "_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": 10,
+    "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,
+    "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
+}

LunarLander-v2_model/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7bec6edb723c361bf94e81f3296eb05df3a82d8a0ee060b7da51615f844613a7
+size 87929

LunarLander-v2_model/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6efc114e08e309180c126ecd8b20750ebe883ac670f49cf331c5f7963976e0d6
+size 43329

LunarLander-v2_model/pytorch_variables.pth

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

LunarLander-v2_model/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.10.11
+- Stable-Baselines3: 1.8.0
+- PyTorch: 2.0.0+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- 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: -21.78 +/- 63.96
+      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 0x7f09c09bfbe0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f09c09bfc70>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f09c09bfd00>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f09c09bfd90>", "_build": "<function ActorCriticPolicy._build at 0x7f09c09bfe20>", "forward": "<function ActorCriticPolicy.forward at 0x7f09c09bfeb0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f09c09bff40>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f09c09c8040>", "_predict": "<function ActorCriticPolicy._predict at 0x7f09c09c80d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f09c09c8160>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f09c09c81f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f09c09c8280>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f09c09c1fc0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 32768, "_total_timesteps": 100, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1682699334841522820, "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": -326.68, "_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": 10, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAAAAAAAAAACUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAAAAAAAAAAAlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu", "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, "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-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.10.11", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -21.782968337398778, "std_reward": 63.96108814638931, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-28T16:31:39.670304"}