Default PPO LunarLander-v2 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: 256.96 +/- 24.62
+      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 0x7f54424f8d30>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f54424f8dc0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f54424f8e50>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f54424f8ee0>", "_build": "<function ActorCriticPolicy._build at 0x7f54424f8f70>", "forward": "<function ActorCriticPolicy.forward at 0x7f54424fc040>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f54424fc0d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f54424fc160>", "_predict": "<function ActorCriticPolicy._predict at 0x7f54424fc1f0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f54424fc280>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f54424fc310>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f54424fc3a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f54424fa5c0>"}, "verbose": 1, "policy_kwargs": {}, "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, "num_timesteps": 1015808, "_total_timesteps": 1000000.0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1678624108382618152, "learning_rate": 0.0003, "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": -0.015808000000000044, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "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, "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": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b4b57692c974533540431e6e5288520b5fe32ba8de5a09d976294d2428f3f6c5
+size 147419

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

ppo-LunarLander-v2/data

@@ -0,0 +1,95 @@
+{
+    "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 0x7f54424f8d30>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f54424f8dc0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f54424f8e50>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f54424f8ee0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f54424f8f70>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f54424fc040>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f54424fc0d0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f54424fc160>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f54424fc1f0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f54424fc280>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f54424fc310>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f54424fc3a0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f54424fa5c0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "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,
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000.0,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1678624108382618152,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/M6kqMFUyYYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
+    },
+    "_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,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 248,
+    "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
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c555c39c48c1be455d607146d3f3ee8325db07dc5f1ab3abfe170d1772fb5e7
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6d08c0bd37d5a346697a91fee22fccd91fc060dab2ce5f09847f827479e13951
+size 43393

ppo-LunarLander-v2/pytorch_variables.pth

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

ppo-LunarLander-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: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 256.9618590576713, "std_reward": 24.623149723685174, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-12T12:53:29.909990"}