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: 259.42 +/- 14.64
+      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 0x7fa2d71a6550>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa2d71a65e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa2d71a6670>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa2d71a6700>", "_build": "<function ActorCriticPolicy._build at 0x7fa2d71a6790>", "forward": "<function ActorCriticPolicy.forward at 0x7fa2d71a6820>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fa2d71a68b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa2d71a6940>", "_predict": "<function ActorCriticPolicy._predict at 0x7fa2d71a69d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa2d71a6a60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa2d71a6af0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa2d71a6b80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fa2d71a8200>"}, "verbose": 1, "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:": "gAWViAAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1678617900707554503, "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": -0.015808000000000044, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "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:": "gAWV0wIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMUC9vcHQvY29uZGEvZW52cy9ybC9saWIvcHl0aG9uMy45L3NpdGUtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5RLgkMCAAGUjAN2YWyUhZQpdJRSlH2UKIwLX19wYWNrYWdlX1+UjBhzdGFibGVfYmFzZWxpbmVzMy5jb21tb26UjAhfX25hbWVfX5SMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMCF9fZmlsZV9flIxQL29wdC9jb25kYS9lbnZzL3JsL2xpYi9weXRob24zLjkvc2l0ZS1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUdU5OaACMEF9tYWtlX2VtcHR5X2NlbGyUk5QpUpSFlHSUUpSMHGNsb3VkcGlja2xlLmNsb3VkcGlja2xlX2Zhc3SUjBJfZnVuY3Rpb25fc2V0c3RhdGWUk5RoH32UfZQoaBZoDYwMX19xdWFsbmFtZV9flIwZY29uc3RhbnRfZm4uPGxvY2Fscz4uZnVuY5SMD19fYW5ub3RhdGlvbnNfX5R9lIwOX19rd2RlZmF1bHRzX1+UTowMX19kZWZhdWx0c19flE6MCl9fbW9kdWxlX1+UaBeMB19fZG9jX1+UTowLX19jbG9zdXJlX1+UaACMCl9tYWtlX2NlbGyUk5RHP8mZmZmZmZqFlFKUhZSMF19jbG91ZHBpY2tsZV9zdWJtb2R1bGVzlF2UjAtfX2dsb2JhbHNfX5R9lHWGlIZSMC4="}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-4.14.305-227.531.amzn2.x86_64-x86_64-with-glibc2.26 # 1 SMP Tue Feb 14 09:55:28 UTC 2023", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu117", "GPU Enabled": "True", "Numpy": "1.24.2", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:682f97fd72b111b72678a2a603ee6289230c9ada123247041381d2580f490f89
+size 147617

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

ppo-LunarLander-v2/data

@@ -0,0 +1,95 @@
+{
+    "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 0x7fa2d71a6550>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa2d71a65e0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa2d71a6670>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa2d71a6700>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fa2d71a6790>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fa2d71a6820>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fa2d71a68b0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa2d71a6940>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fa2d71a69d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa2d71a6a60>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa2d71a6af0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa2d71a6b80>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fa2d71a8200>"
+    },
+    "verbose": 1,
+    "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:": "gAWViAAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": 4,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1678617900707554503,
+    "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": -0.015808000000000044,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 248,
+    "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
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:46b43df66c4f544b3436f0a9cec1cc4726b1e4639dd178fbff96da9f987c0c5c
+size 88057

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:927ba04dbdf891112d884cd18f3905ad7739f2c3fa0b0efc3240ff56431e1627
+size 43393

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 +1,7 @@
+- OS: Linux-4.14.305-227.531.amzn2.x86_64-x86_64-with-glibc2.26 # 1 SMP Tue Feb 14 09:55:28 UTC 2023
+- Python: 3.9.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu117
+- GPU Enabled: True
+- Numpy: 1.24.2
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 259.4173726146816, "std_reward": 14.64084872890249, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-12T11:30:52.646943"}