Last model developed

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: 222.91 +/- 33.77
+      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 0x7f8cfecf00d0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f8cfecf0160>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f8cfecf01f0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f8cfecf0280>", "_build": "<function ActorCriticPolicy._build at 0x7f8cfecf0310>", "forward": "<function ActorCriticPolicy.forward at 0x7f8cfecf03a0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f8cfecf0430>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f8cfecf04c0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f8cfecf0550>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f8cfecf05e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f8cfecf0670>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f8cfecf0700>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f8cfecebdc0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 493504, "_total_timesteps": 497872, "_num_timesteps_at_start": 487872, "seed": null, "action_noise": null, "start_time": 1681462289188519964, "learning_rate": 0.0, "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": -0.013619141891661357, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 116, "n_steps": 1024, "gamma": 1, "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, "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, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

ppo-Lunar_lander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0188addd55a99e4d49f47009a0932fa07f9bd309c6427e9826e199b5d126a00b
+size 147159

ppo-Lunar_lander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.8.0

ppo-Lunar_lander-v2/data

@@ -0,0 +1,96 @@
+{
+    "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 0x7f8cfecf00d0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f8cfecf0160>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f8cfecf01f0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f8cfecf0280>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f8cfecf0310>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f8cfecf03a0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f8cfecf0430>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f8cfecf04c0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f8cfecf0550>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f8cfecf05e0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f8cfecf0670>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f8cfecf0700>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f8cfecebdc0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 493504,
+    "_total_timesteps": 497872,
+    "_num_timesteps_at_start": 487872,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1681462289188519964,
+    "learning_rate": 0.0,
+    "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": -0.013619141891661357,
+    "_stats_window_size": 100,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 116,
+    "n_steps": 1024,
+    "gamma": 1,
+    "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,
+    "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
+}

ppo-Lunar_lander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:592ae042bb74ad0965697194ae3934e63bc78a0d124d81239a53c8d388c50b85
+size 88057

ppo-Lunar_lander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a471a1b363eaf22932bf2d0a41fb5d1c8b3f15269d82a2e69989b58ff300f450
+size 43329

ppo-Lunar_lander-v2/pytorch_variables.pth

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

ppo-Lunar_lander-v2/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.9.16
+- Stable-Baselines3: 1.8.0
+- PyTorch: 2.0.0+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 222.91321764535988, "std_reward": 33.76667381362599, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-14T09:25:24.512140"}