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: 259.96 +/- 21.64
+      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 0x7b21fef32680>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b21fef32710>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7b21fef327a0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7b21fef32830>", "_build": "<function ActorCriticPolicy._build at 0x7b21fef328c0>", "forward": "<function ActorCriticPolicy.forward at 0x7b21fef32950>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7b21fef329e0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7b21fef32a70>", "_predict": "<function ActorCriticPolicy._predict at 0x7b21fef32b00>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7b21fef32b90>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7b21fef32c20>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7b21fef32cb0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7b21fef34c80>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1699981116444801453, "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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"}, "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 sha256:5980ceb9f0d5349d9cfc5c8dcc7682800a19c59aaf6057cb62054b1e0aaff8f2
+size 148050

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 0x7b21fef32680>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b21fef32710>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7b21fef327a0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7b21fef32830>",
+        "_build": "<function ActorCriticPolicy._build at 0x7b21fef328c0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7b21fef32950>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7b21fef329e0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7b21fef32a70>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7b21fef32b00>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7b21fef32b90>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7b21fef32c20>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7b21fef32cb0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7b21fef34c80>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1699981116444801453,
+    "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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"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1405e40e2e1d18fb857ac1e90a3fd435df321a3b75085160a1263f39259d4177
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c3c57c24ae65d79c1df254a4c94036bc301af29b87e5158324c540afa2726a8e
+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": 259.95788851952966, "std_reward": 21.64217892193829, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-11-14T17:24:57.422152"}