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: -214.99 +/- 156.32
+      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 0x7f75bad2fe50>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f75bad2fee0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f75bad2ff70>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f75bad31040>", "_build": "<function ActorCriticPolicy._build at 0x7f75bad310d0>", "forward": "<function ActorCriticPolicy.forward at 0x7f75bad31160>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f75bad311f0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f75bad31280>", "_predict": "<function ActorCriticPolicy._predict at 0x7f75bad31310>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f75bad313a0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f75bad31430>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f75bad314c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f75bad324c0>"}, "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": 16384, "_total_timesteps": 1000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1680610879439145777, "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": -15.384, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 4, "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-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ebcb4349f7c889c8fbe030f49910d1d6112fd088a9a523be6dea8fe6120da303
+size 147275

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 0x7f75bad2fe50>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f75bad2fee0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f75bad2ff70>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f75bad31040>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f75bad310d0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f75bad31160>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f75bad311f0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f75bad31280>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f75bad31310>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f75bad313a0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f75bad31430>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f75bad314c0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f75bad324c0>"
+    },
+    "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": 16384,
+    "_total_timesteps": 1000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1680610879439145777,
+    "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": -15.384,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 4,
+    "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 sha256:524e0633f1ab3d7b9e1deccb545277a002d7270f021d56469e3bb51a449f6943
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c68aedd26c7c85a73b1de36c4a0311805a2a9d3acf71ed87b362f160f3b2bdfa
+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-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.9.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 2.0.0+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": -214.99337575818646, "std_reward": 156.31753163834188, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-04T12:25:26.161377"}