Upload PPO LunarLander-v2 trained agent (Lunar200K)

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: -154.66 +/- 38.62
+      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 0x7f78aec76280>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f78aec76310>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f78aec763a0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f78aec76430>", "_build": "<function ActorCriticPolicy._build at 0x7f78aec764c0>", "forward": "<function ActorCriticPolicy.forward at 0x7f78aec76550>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f78aec765e0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f78aec76670>", "_predict": "<function ActorCriticPolicy._predict at 0x7f78aec76700>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f78aec76790>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f78aec76820>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f78aec768b0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f78aec74b00>"}, "verbose": 1, "policy_kwargs": {}, "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:": "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", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": "Generator(PCG64)"}, "n_envs": 16, "num_timesteps": 212992, "_total_timesteps": 200000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1679966179141618000, "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": -0.0649599999999999, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 52, "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, "system_info": {"OS": "macOS-10.16-x86_64-i386-64bit Darwin Kernel Version 22.3.0: Mon Jan 30 20:38:37 PST 2023; root:xnu-8792.81.3~2/RELEASE_ARM64_T6000", "Python": "3.9.16", "Stable-Baselines3": "2.0.0a0", "PyTorch": "2.0.0", "GPU Enabled": "False", "Numpy": "1.24.2", "Gym": "0.28.1"}}

ppo-LunarLander-v2-Lunar200K.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:915df3118d9bfc0a4794739ffd2cf8df36be1332daf67c6f05515708525b91c5
+size 146527

ppo-LunarLander-v2-Lunar200K/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0a0

ppo-LunarLander-v2-Lunar200K/data

@@ -0,0 +1,98 @@
+{
+    "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 0x7f78aec76280>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f78aec76310>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f78aec763a0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f78aec76430>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f78aec764c0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f78aec76550>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f78aec765e0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f78aec76670>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f78aec76700>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f78aec76790>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f78aec76820>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f78aec768b0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f78aec74b00>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "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:": "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",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": "Generator(PCG64)"
+    },
+    "n_envs": 16,
+    "num_timesteps": 212992,
+    "_total_timesteps": 200000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1679966179141618000,
+    "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": -0.0649599999999999,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 52,
+    "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:": "gAWVAwMAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMaC9Vc2Vycy9qZXJlbXlyb3RoL29wdC9hbmFjb25kYTMvZW52cy9MdW5hci9saWIvcHl0aG9uMy45L3NpdGUtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5RLg0MCAAGUjAN2YWyUhZQpdJRSlH2UKIwLX19wYWNrYWdlX1+UjBhzdGFibGVfYmFzZWxpbmVzMy5jb21tb26UjAhfX25hbWVfX5SMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMCF9fZmlsZV9flIxoL1VzZXJzL2plcmVteXJvdGgvb3B0L2FuYWNvbmRhMy9lbnZzL0x1bmFyL2xpYi9weXRob24zLjkvc2l0ZS1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUdU5OaACMEF9tYWtlX2VtcHR5X2NlbGyUk5QpUpSFlHSUUpSMHGNsb3VkcGlja2xlLmNsb3VkcGlja2xlX2Zhc3SUjBJfZnVuY3Rpb25fc2V0c3RhdGWUk5RoH32UfZQoaBZoDYwMX19xdWFsbmFtZV9flIwZY29uc3RhbnRfZm4uPGxvY2Fscz4uZnVuY5SMD19fYW5ub3RhdGlvbnNfX5R9lIwOX19rd2RlZmF1bHRzX1+UTowMX19kZWZhdWx0c19flE6MCl9fbW9kdWxlX1+UaBeMB19fZG9jX1+UTowLX19jbG9zdXJlX1+UaACMCl9tYWtlX2NlbGyUk5RHP8mZmZmZmZqFlFKUhZSMF19jbG91ZHBpY2tsZV9zdWJtb2R1bGVzlF2UjAtfX2dsb2JhbHNfX5R9lHWGlIZSMC4="
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

ppo-LunarLander-v2-Lunar200K/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:93c2b4452bf7971b71e66d943962679e2628961965af67bbfa73f22b77333a62
+size 87545

ppo-LunarLander-v2-Lunar200K/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:50c27a5f55a668fbd20d36d0357483ccdfa0b2680c0062acfcd2b44843ee8201
+size 43201

ppo-LunarLander-v2-Lunar200K/pytorch_variables.pth

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

ppo-LunarLander-v2-Lunar200K/system_info.txt

@@ -0,0 +1,7 @@
+- OS: macOS-10.16-x86_64-i386-64bit Darwin Kernel Version 22.3.0: Mon Jan 30 20:38:37 PST 2023; root:xnu-8792.81.3~2/RELEASE_ARM64_T6000
+- Python: 3.9.16
+- Stable-Baselines3: 2.0.0a0
+- PyTorch: 2.0.0
+- GPU Enabled: False
+- Numpy: 1.24.2
+- Gym: 0.28.1

results.json

@@ -0,0 +1 @@
+{"mean_reward": -154.6552431, "std_reward": 38.618635772366, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-27T21:34:26.448760"}