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: 214.77 +/- 43.73
+      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 0x785bd82f3d90>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x785bd82f3e20>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x785bd82f3eb0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x785bd82f3f40>", "_build": "<function ActorCriticPolicy._build at 0x785bd82f4040>", "forward": "<function ActorCriticPolicy.forward at 0x785bd82f40d0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x785bd82f4160>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x785bd82f41f0>", "_predict": "<function ActorCriticPolicy._predict at 0x785bd82f4280>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x785bd82f4310>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x785bd82f43a0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x785bd82f4430>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x785bd8491a80>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1000448, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1702288232001489014, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAGaB8Dwp0By6rrOIOyJD0LbgjEA6sFrKtQAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": -0.00044800000000000395, "_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": 3908, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_shape": [8], "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "_np_random": "Generator(PCG64)"}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 1, "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, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.1.0+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:631f4fb51cb3f1745c7bfd5cd0ee07c44907799ec5755bfa28243aa4bf357adf
+size 147638

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0a5

ppo-LunarLander-v2/data

@@ -0,0 +1,99 @@
+{
+    "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 0x785bd82f3d90>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x785bd82f3e20>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x785bd82f3eb0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x785bd82f3f40>",
+        "_build": "<function ActorCriticPolicy._build at 0x785bd82f4040>",
+        "forward": "<function ActorCriticPolicy.forward at 0x785bd82f40d0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x785bd82f4160>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x785bd82f41f0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x785bd82f4280>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x785bd82f4310>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x785bd82f43a0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x785bd82f4430>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x785bd8491a80>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1000448,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1702288232001489014,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAGaB8Dwp0By6rrOIOyJD0LbgjEA6sFrKtQAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": -0.00044800000000000395,
+    "_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": 3908,
+    "observation_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True]",
+        "_shape": [
+            8
+        ],
+        "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "_np_random": "Generator(PCG64)"
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 1,
+    "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,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fe7d15520dcd602ffeff155bd60fcd514aac94b95038405251980693420008b3
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1d5ef2e109c7189853dd3e626dac8b8ba80e839bcc7fb88b71a5e4d44bda5d83
+size 43762

ppo-LunarLander-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
+size 864

ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.1.0+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 214.77240653679533, "std_reward": 43.73262887511806, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-12-11T10:24:51.627901"}