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: 278.91 +/- 14.58
+      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 0x7dd8fed8ff40>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7dd8fed94040>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7dd8fed940d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7dd8fed94160>", "_build": "<function ActorCriticPolicy._build at 0x7dd8fed941f0>", "forward": "<function ActorCriticPolicy.forward at 0x7dd8fed94280>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7dd8fed94310>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7dd8fed943a0>", "_predict": "<function ActorCriticPolicy._predict at 0x7dd8fed94430>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7dd8fed944c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7dd8fed94550>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7dd8fed945e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7dd8fef29480>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1507328, "_total_timesteps": 1500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1711300421607133239, "learning_rate": 0.0003, "tensorboard_log": null, "_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": -0.004885333333333408, "_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": 368, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "gAWVdgIAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWCAAAAAAAAAABAQEBAQEBAZRoCIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksIhZSMAUOUdJRSlIwNYm91bmRlZF9hYm92ZZRoESiWCAAAAAAAAAABAQEBAQEBAZRoFUsIhZRoGXSUUpSMBl9zaGFwZZRLCIWUjANsb3eUaBEoliAAAAAAAAAAAAC0wgAAtMIAAKDAAACgwNsPScAAAKDAAAAAgAAAAICUaAtLCIWUaBl0lFKUjARoaWdolGgRKJYgAAAAAAAAAAAAtEIAALRCAACgQAAAoEDbD0lAAACgQAAAgD8AAIA/lGgLSwiFlGgZdJRSlIwIbG93X3JlcHKUjFtbLTkwLiAgICAgICAgLTkwLiAgICAgICAgIC01LiAgICAgICAgIC01LiAgICAgICAgIC0zLjE0MTU5MjcgIC01LgogIC0wLiAgICAgICAgIC0wLiAgICAgICBdlIwJaGlnaF9yZXBylIxTWzkwLiAgICAgICAgOTAuICAgICAgICAgNS4gICAgICAgICA1LiAgICAgICAgIDMuMTQxNTkyNyAgNS4KICAxLiAgICAgICAgIDEuICAgICAgIF2UjApfbnBfcmFuZG9tlE51Yi4=", "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": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "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-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.2.1+cu121", "GPU Enabled": "True", "Numpy": "1.25.2", "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 sha256:7a14d6e0ce66c1c1805acef66ca8d83fa7a34d24656edd54b67eae6e899714d5
+size 148012

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 0x7dd8fed8ff40>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7dd8fed94040>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7dd8fed940d0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7dd8fed94160>",
+        "_build": "<function ActorCriticPolicy._build at 0x7dd8fed941f0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7dd8fed94280>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7dd8fed94310>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7dd8fed943a0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7dd8fed94430>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7dd8fed944c0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7dd8fed94550>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7dd8fed945e0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7dd8fef29480>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1507328,
+    "_total_timesteps": 1500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1711300421607133239,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_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": -0.004885333333333408,
+    "_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": 368,
+    "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": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "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:cb1427a381dcfc61af4a61636cab6e080c262d4f4b962e5c3acba5f7a81e0805
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bd42bfcc88f6272683b8b6374a157d15bcd4a49ee8320724bcc77691f1baa9ce
+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-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.2.1+cu121
+- GPU Enabled: True
+- Numpy: 1.25.2
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 278.90830656471155, "std_reward": 14.575234416372405, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-03-24T18:07:46.598330"}