First PPO model on LunarLander-v2 env

README.md

@@ -0,0 +1,36 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 183.96 +/- 75.63
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+---
+
+# **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 0x7ffaec3ed170>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ffaec3ed200>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ffaec3ed290>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ffaec3ed320>", "_build": "<function ActorCriticPolicy._build at 0x7ffaec3ed3b0>", "forward": "<function ActorCriticPolicy.forward at 0x7ffaec3ed440>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ffaec3ed4d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7ffaec3ed560>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ffaec3ed5f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ffaec3ed680>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ffaec3ed710>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7ffaec43e300>"}, "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:": "gAWVgQAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwFc2hhcGWUKYwFZHR5cGWUjAVudW1weZRoB5OUjAJpOJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRijApfbnBfcmFuZG9tlE51Yi4=", "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": 1660665843.028033, "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-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022", "Python": "3.7.13", "Stable-Baselines3": "1.6.0", "PyTorch": "1.12.1+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.17.3"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:284d93d21f758e9b669c3c0e26d1b8ec009ce62bdf1dd7d06f42efc8a136cf99
+size 147145

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.0

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 0x7ffaec3ed170>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ffaec3ed200>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ffaec3ed290>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ffaec3ed320>",
+        "_build": "<function ActorCriticPolicy._build at 0x7ffaec3ed3b0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7ffaec3ed440>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ffaec3ed4d0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7ffaec3ed560>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ffaec3ed5f0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ffaec3ed680>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ffaec3ed710>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7ffaec43e300>"
+    },
+    "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:": "gAWVgQAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwFc2hhcGWUKYwFZHR5cGWUjAVudW1weZRoB5OUjAJpOJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRijApfbnBfcmFuZG9tlE51Yi4=",
+        "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": 1660665843.028033,
+    "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:fd6a50025f8d0b733243d09c03aef0d5472526c0b971d40c7d7add591f55a620
+size 87865

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:704df5b7d033e261747fab6fca78df1b850fe07aa4f0468ffc884f0d4a191a0c
+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-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
+Python: 3.7.13
+Stable-Baselines3: 1.6.0
+PyTorch: 1.12.1+cu113
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.17.3

results.json

@@ -0,0 +1 @@
+{"mean_reward": 183.95836045554074, "std_reward": 75.634556242684, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-08-16T16:21:27.390574"}