Upload PPO LunarLander-v201 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: 266.91 +/- 17.62
+      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 0x7fbe0688f280>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbe0688f310>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbe0688f3a0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbe0688f430>", "_build": "<function ActorCriticPolicy._build at 0x7fbe0688f4c0>", "forward": "<function ActorCriticPolicy.forward at 0x7fbe0688f550>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbe0688f5e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fbe0688f670>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbe0688f700>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbe0688f790>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbe0688f820>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fbe068900c0>"}, "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": 64, "num_timesteps": 524288, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1671366545836487477, "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:": "gAWVswAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJZAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiS0CFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.04857599999999995, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 236, "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": "False", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo-LunarLander-v201.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:87bc62db6dbc2471b3cf5d6e21eada98f9c14c208fa7ee48782efbd6f92ed1ed
+size 148700

ppo-LunarLander-v201/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.2

ppo-LunarLander-v201/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 0x7fbe0688f280>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbe0688f310>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbe0688f3a0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbe0688f430>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fbe0688f4c0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fbe0688f550>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbe0688f5e0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fbe0688f670>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbe0688f700>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbe0688f790>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbe0688f820>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fbe068900c0>"
+    },
+    "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": 64,
+    "num_timesteps": 524288,
+    "_total_timesteps": 500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1671366545836487477,
+    "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:": "gAWVswAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJZAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiS0CFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.04857599999999995,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 236,
+    "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-v201/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8b21890062461e5fc9e9ec4acc28270b8095e1298ff571e6d216463827ac55dc
+size 87545

ppo-LunarLander-v201/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cca50e9620f9b614379389aaf8a34d514950d7176c462b96a68d054ef01004bc
+size 43073

ppo-LunarLander-v201/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

ppo-LunarLander-v201/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: False
+Numpy: 1.21.6
+Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 266.9055809058781, "std_reward": 17.622740722738044, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-18T12:36:35.146756"}