Upload PPO LunarLander-v2 agent - 2M steps

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: 234.86 +/- 19.06
+      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 0x7f11aad43670>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f11aad43700>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f11aad43790>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f11aad43820>", "_build": "<function ActorCriticPolicy._build at 0x7f11aad438b0>", "forward": "<function ActorCriticPolicy.forward at 0x7f11aad43940>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f11aad439d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f11aad43a60>", "_predict": "<function ActorCriticPolicy._predict at 0x7f11aad43af0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f11aad43b80>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f11aad43c10>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f11aad43ca0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f11aad3d8d0>"}, "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": 1, "num_timesteps": 1000448, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1676578593732058330, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAM03sz17QpG6k8kxtGxKMzFtlgW6ewSOMwAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 3908, "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-5.15.0-1033-azure-x86_64-with-glibc2.29 # 40~20.04.1-Ubuntu SMP Tue Jan 24 16:06:28 UTC 2023", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu117", "GPU Enabled": "False", "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:d68c1caa9b1096561773b1276af3c988c68b55ec81ca06cd7af79cdb19c970b6
+size 146123

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 0x7f11aad43670>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f11aad43700>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f11aad43790>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f11aad43820>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f11aad438b0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f11aad43940>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f11aad439d0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f11aad43a60>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f11aad43af0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f11aad43b80>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f11aad43c10>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f11aad43ca0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f11aad3d8d0>"
+    },
+    "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": 1,
+    "num_timesteps": 1000448,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1676578593732058330,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAM03sz17QpG6k8kxtGxKMzFtlgW6ewSOMwAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 3908,
+    "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 sha256:6f01ad2293384eb41d2a803d066626ecb9fa4a32e9335c76ed52fd7bdf3b0064
+size 87545

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7d9658f44c58c5e3359868580289a8e99322b96f662cc51736a88cd68030933c
+size 43265

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-5.15.0-1033-azure-x86_64-with-glibc2.29 # 40~20.04.1-Ubuntu SMP Tue Jan 24 16:06:28 UTC 2023
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu117
+- GPU Enabled: False
+- Numpy: 1.24.2
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 234.86132086777866, "std_reward": 19.0635706322769, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-16T20:58:02.404784"}