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: 173.31 +/- 64.01
+      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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7f559f339290>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f559f339320>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f559f3393b0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f559f339440>", "_build": "<function ActorCriticPolicy._build at 0x7f559f3394d0>", "forward": "<function ActorCriticPolicy.forward at 0x7f559f339560>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f559f3395f0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f559f339680>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f559f339710>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f559f3397a0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f559f339830>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f559f37eb40>"}, "verbose": 1, "policy_kwargs": {}, "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, "num_timesteps": 507904, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1666268862061228687, "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": 124, "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-5.10.133+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.7.15", "Stable-Baselines3": "1.6.2", "PyTorch": "1.12.1+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6ff2d590289643d2df6a597a45084ebb0794f582f8bb1601c283ef164abee158
+size 147154

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.2

ppo-LunarLander-v2/data

@@ -0,0 +1,94 @@
+{
+    "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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7f559f339290>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f559f339320>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f559f3393b0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f559f339440>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f559f3394d0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f559f339560>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f559f3395f0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f559f339680>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f559f339710>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f559f3397a0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f559f339830>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f559f37eb40>"
+    },
+    "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:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": 4,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "num_timesteps": 507904,
+    "_total_timesteps": 500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1666268862061228687,
+    "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": 124,
+    "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 sha256:57b0386adc1a1844b2026d8f4848a7d180425e4fa6c1c05fab87e4a2af5f8ac6
+size 87865

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:abb1d5b3e8b017e5b0c1cf3875050f07b6bbc4a9b8929992d5f5307bb444b942
+size 43201

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-5.10.133+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Fri Aug 26 08:44:51 UTC 2022
+Python: 3.7.15
+Stable-Baselines3: 1.6.2
+PyTorch: 1.12.1+cu113
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 173.30526899048965, "std_reward": 64.01316970214077, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-10-20T12:50:10.654660"}