Upload PPO LunarLander-v2 trained agent

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: 243.07 +/- 65.28
+      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 0x7fc7b4423a30>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc7b4423ac0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc7b4423b50>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc7b4423be0>", "_build": "<function ActorCriticPolicy._build at 0x7fc7b4423c70>", "forward": "<function ActorCriticPolicy.forward at 0x7fc7b4423d00>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fc7b4423d90>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc7b4423e20>", "_predict": "<function ActorCriticPolicy._predict at 0x7fc7b4423eb0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc7b4423f40>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc7b442c040>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc7b442c0d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fc7b4e23780>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1000448, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1689138081876991447, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAMAnpz2f7JC7M3KDuxrEqz1Y/Qu9xR19PgAAgD8AAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.00044800000000000395, "_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": 3908, "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": 1, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 4, "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:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz8zqSowVTJhhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "system_info": {"OS": "Linux-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f6e9171ce90d7454b1225431d280af49e821a879a7115c0b9ecefe39ed50a0ee
+size 146061

ppo-LunarLander-v2/_stable_baselines3_version

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

ppo-LunarLander-v2/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 0x7fc7b4423a30>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc7b4423ac0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc7b4423b50>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc7b4423be0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fc7b4423c70>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fc7b4423d00>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fc7b4423d90>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc7b4423e20>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fc7b4423eb0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc7b4423f40>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc7b442c040>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc7b442c0d0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fc7b4e23780>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1000448,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1689138081876991447,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAMAnpz2f7JC7M3KDuxrEqz1Y/Qu9xR19PgAAgD8AAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.00044800000000000395,
+    "_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": 3908,
+    "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": 1,
+    "n_steps": 1024,
+    "gamma": 0.999,
+    "gae_lambda": 0.98,
+    "ent_coef": 0.01,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 4,
+    "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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"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0949d6d0afdbee45269e8a4afeea8b5cc4ff8fb392d700587a5ea12d757d3c8e
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:95aca693253c26458fe667db7e94ef88b642b433bb2cace52e93dfb79398e7b2
+size 43329

ppo-LunarLander-v2/pytorch_variables.pth

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

ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 243.0689855, "std_reward": 65.27714284456002, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-12T05:48:35.463457"}