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: 265.00 +/- 16.90
+      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 0x7fa99cadb550>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa99cadb5e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa99cadb670>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa99cadb700>", "_build": "<function ActorCriticPolicy._build at 0x7fa99cadb790>", "forward": "<function ActorCriticPolicy.forward at 0x7fa99cadb820>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fa99cadb8b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa99cadb940>", "_predict": "<function ActorCriticPolicy._predict at 0x7fa99cadb9d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa99cadba60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa99cadbaf0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa99cadbb80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fa99cad5810>"}, "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": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1673795766689484952, "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, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.27 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.0+cu116", "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 sha256:555cd330f1eaa20814a870918e9c75993ccf85cf918ade25e11d81dbfe471c40
+size 147416

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 0x7fa99cadb550>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa99cadb5e0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa99cadb670>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa99cadb700>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fa99cadb790>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fa99cadb820>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fa99cadb8b0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa99cadb940>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fa99cadb9d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa99cadba60>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa99cadbaf0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa99cadbb80>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fa99cad5810>"
+    },
+    "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": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1673795766689484952,
+    "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 sha256:845f675a3213276d1cdbe25f4cd52ac52d52b763544d004a9fc3de76b3e83687
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8fd1a8d4b352d4fa480cc8a9feedcf0b3fe71a1d196f77657e418e2a1064b292
+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-glibc2.27 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.0+cu116
+- GPU Enabled: True
+- Numpy: 1.21.6
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 264.9976443012157, "std_reward": 16.89609486983061, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-15T15:38:32.569823"}