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: 261.15 +/- 15.04
+      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 0x7d8b574311b0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d8b57431240>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d8b574312d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d8b57431360>", "_build": "<function ActorCriticPolicy._build at 0x7d8b574313f0>", "forward": "<function ActorCriticPolicy.forward at 0x7d8b57431480>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d8b57431510>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d8b574315a0>", "_predict": "<function ActorCriticPolicy._predict at 0x7d8b57431630>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d8b574316c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d8b57431750>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d8b574317e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7d8b57438ac0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1700920495710306963, "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": -0.015808000000000044, "_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": 248, "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:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "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:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "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 sha256:6d3b8971acccf8d7477bd1f8e5712e2b878c5ae6d1d4664a5af98b0c7395e74d
+size 148014

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 0x7d8b574311b0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d8b57431240>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d8b574312d0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d8b57431360>",
+        "_build": "<function ActorCriticPolicy._build at 0x7d8b574313f0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7d8b57431480>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d8b57431510>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d8b574315a0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7d8b57431630>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d8b574316c0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d8b57431750>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d8b574317e0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7d8b57438ac0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1700920495710306963,
+    "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": -0.015808000000000044,
+    "_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": 248,
+    "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:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "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 sha256:447246b04bd88f0341026394702c1a5807749abe40ef2ca1683b09d5a6fb41c5
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:887fc3402a2522874ccf02d61aa19e6cde913bcf9afeff02bb0a2f18464b2e17
+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": 261.14701874604464, "std_reward": 15.040848430541772, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-11-25T14:24:02.354837"}