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: 267.55 +/- 24.06
+      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 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 0x7fe3dc8935e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe3dc893670>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe3dc893700>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe3dc893790>", "_build": "<function ActorCriticPolicy._build at 0x7fe3dc893820>", "forward": "<function ActorCriticPolicy.forward at 0x7fe3dc8938b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe3dc893940>", "_predict": "<function ActorCriticPolicy._predict at 0x7fe3dc8939d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe3dc893a60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe3dc893af0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe3dc893b80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fe3dc88ae40>"}, "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": 1670722288389650134, "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-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.6.2", "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 sha256:4592bf575e87d091e83a15459de7a39629ccb808a9748938ce60b91b3e5a8355
+size 147206

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 0x7fe3dc8935e0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe3dc893670>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe3dc893700>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe3dc893790>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fe3dc893820>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fe3dc8938b0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe3dc893940>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fe3dc8939d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe3dc893a60>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe3dc893af0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe3dc893b80>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fe3dc88ae40>"
+    },
+    "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": 1670722288389650134,
+    "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:e7788ac946aaeaf8e71a0a01d239afeff51494a2a07b451348fe663881a6a93a
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:46d109271e19054d064d8b8ab1fa7fef371954b20c749d3bf4be430453148129
+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-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
+Python: 3.8.16
+Stable-Baselines3: 1.6.2
+PyTorch: 1.13.0+cu116
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 267.5516890837807, "std_reward": 24.0562880375094, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-11T02:19:54.727574"}