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: 257.43 +/- 16.15
+      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 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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 0x7f308a711af0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f308a711b80>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f308a711c10>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f308a711ca0>", "_build": "<function ActorCriticPolicy._build at 0x7f308a711d30>", "forward": "<function ActorCriticPolicy.forward at 0x7f308a711dc0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f308a711e50>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f308a711ee0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f308a711f70>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f308a713040>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f308a7130d0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f308a713160>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f308a708d20>"}, "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": 1674440992412278457, "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-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+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:cb9d8983f9ed4b3cfe86275e6c90648b450d1d294b015900bdaec008b58f46c7
+size 147420

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

ppo-LunarLander-v2/data

@@ -0,0 +1,95 @@
+{
+    "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 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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 0x7f308a711af0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f308a711b80>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f308a711c10>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f308a711ca0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f308a711d30>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f308a711dc0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f308a711e50>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f308a711ee0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f308a711f70>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f308a713040>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f308a7130d0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f308a713160>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f308a708d20>"
+    },
+    "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": 1674440992412278457,
+    "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 sha256:15d251459faf8b1a6ff6c8fa6039eb05c434fce5d40e01f43377579768e436b8
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:621b7217f0dee30d69ec372f80fe90636cf061d0e5664a882a5d5626815db4b0
+size 43393

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-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.21.6
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 257.4315436102423, "std_reward": 16.15336073896689, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-23T03:13:11.610000"}