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: 259.17 +/- 18.54
+      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 0x7f55a9350ee0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f55a9350f70>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f55a9354040>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f55a93540d0>", "_build": "<function ActorCriticPolicy._build at 0x7f55a9354160>", "forward": "<function ActorCriticPolicy.forward at 0x7f55a93541f0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f55a9354280>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f55a9354310>", "_predict": "<function ActorCriticPolicy._predict at 0x7f55a93543a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f55a9354430>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f55a93544c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f55a9354550>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f55a9355440>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1683036549700344338, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": null, "_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, "_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": 248, "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": 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, "system_info": {"OS": "Linux-5.4.0-122-generic-x86_64-with-glibc2.31 # 138~18.04.1-Ubuntu SMP Fri Jun 24 14:14:03 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.8.0", "PyTorch": "1.12.1+cu116", "GPU Enabled": "True", "Numpy": "1.23.4", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b23e1a0892d7823245813ddcadcadd04410d396a86024125ad5cf01ee06156ec
+size 146545

ppo-LunarLander-v2/_stable_baselines3_version

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

ppo-LunarLander-v2/data

@@ -0,0 +1,93 @@
+{
+    "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 0x7f55a9350ee0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f55a9350f70>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f55a9354040>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f55a93540d0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f55a9354160>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f55a93541f0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f55a9354280>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f55a9354310>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f55a93543a0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f55a9354430>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f55a93544c0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f55a9354550>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f55a9355440>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1683036549700344338,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": null,
+    "_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,
+    "_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": 248,
+    "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": 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
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b593ccbfddef5e0bcff59dd8b0bd931184423e4cd27e6f420c99ef4696856b5c
+size 87993

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ce975c453c6f227f3aa3e697f9cad8d0a6f873aeaf1537b802f2fe375dfa42d7
+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 +1,7 @@
+- OS: Linux-5.4.0-122-generic-x86_64-with-glibc2.31 # 138~18.04.1-Ubuntu SMP Fri Jun 24 14:14:03 UTC 2022
+- Python: 3.9.16
+- Stable-Baselines3: 1.8.0
+- PyTorch: 1.12.1+cu116
+- GPU Enabled: True
+- Numpy: 1.23.4
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 259.1741572207421, "std_reward": 18.538225670469885, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-05-02T15:17:26.475099"}