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: -22.15 +/- 22.72
+      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 0x7fc2675d3820>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc2675d38b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc2675d3940>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc2675d39d0>", "_build": "<function ActorCriticPolicy._build at 0x7fc2675d3a60>", "forward": "<function ActorCriticPolicy.forward at 0x7fc2675d3af0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc2675d3b80>", "_predict": "<function ActorCriticPolicy._predict at 0x7fc2675d3c10>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc2675d3ca0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc2675d3d30>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc2675d3dc0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fc2675c9db0>"}, "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": 1, "num_timesteps": 200704, "_total_timesteps": 200000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1670941503128457132, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAAAfgD1pUkA+MkghPTrqI70gSAS9vugOuwAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.0035199999999999676, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 784, "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 sha256:91c6d09bcd6b1e45bad98a68fc24f90a8a35eb321182848a96efb28496730fe5
+size 146552

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 0x7fc2675d3820>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc2675d38b0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc2675d3940>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc2675d39d0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fc2675d3a60>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fc2675d3af0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc2675d3b80>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fc2675d3c10>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc2675d3ca0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc2675d3d30>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc2675d3dc0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fc2675c9db0>"
+    },
+    "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": 1,
+    "num_timesteps": 200704,
+    "_total_timesteps": 200000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1670941503128457132,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAAAfgD1pUkA+MkghPTrqI70gSAS9vugOuwAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.0035199999999999676,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 784,
+    "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 sha256:59d09f04028f7a2ac2c020ddcad54fef72a5a8a1b17fd8f0f7061a739d2df61e
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:abbc188f613cf3533f80367be4ac14f05299af9fbe6a0bbf3eba3990d1be67aa
+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": -22.151243872902704, "std_reward": 22.7241970485685, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-13T14:56:29.895262"}