first commit

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: 254.38 +/- 67.15
+      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 0x7f4aae9b4af0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4aae9b4b80>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4aae9b4c10>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4aae9b4ca0>", "_build": "<function ActorCriticPolicy._build at 0x7f4aae9b4d30>", "forward": "<function ActorCriticPolicy.forward at 0x7f4aae9b4dc0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4aae9b4e50>", "_predict": "<function ActorCriticPolicy._predict at 0x7f4aae9b4ee0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4aae9b4f70>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4aae9b8040>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4aae9b80d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f4aae9aee40>"}, "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": 1500160, "_total_timesteps": 1500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1671400777812934921, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAJNGOj6uRX0/d1gnPY792r5DcU4+oMQGvgAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": -0.00010666666666669933, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 5860, "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 sha256:98d31c3a5e2ac73121b0a384a5cc7767e4508b77040eac7ac808b8ca4e585fd2
+size 146512

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 0x7f4aae9b4af0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4aae9b4b80>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4aae9b4c10>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4aae9b4ca0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f4aae9b4d30>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f4aae9b4dc0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4aae9b4e50>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f4aae9b4ee0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4aae9b4f70>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4aae9b8040>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4aae9b80d0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f4aae9aee40>"
+    },
+    "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": 1500160,
+    "_total_timesteps": 1500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1671400777812934921,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAJNGOj6uRX0/d1gnPY792r5DcU4+oMQGvgAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": -0.00010666666666669933,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 5860,
+    "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:cd2606142852841449a4094a6b477565f9690a6eaf97442a5e9ef7e81bb023b1
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:752ebbf696a4f5b5468e3e6b55dc2a107215598eb3dcef2474c88dc9122a4639
+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": 254.37587700720624, "std_reward": 67.1495490703082, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-18T23:00:32.704083"}