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: 262.92 +/- 20.59
+      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 0x7f4b49a7ff70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4b49a83040>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4b49a830d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4b49a83160>", "_build": "<function ActorCriticPolicy._build at 0x7f4b49a831f0>", "forward": "<function ActorCriticPolicy.forward at 0x7f4b49a83280>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f4b49a83310>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4b49a833a0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f4b49a83430>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4b49a834c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4b49a83550>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4b49a835e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f4b49a82680>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1682018864876944371, "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, "_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 '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, "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": "1.8.0", "PyTorch": "2.0.0+cu118", "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 sha256:1aa11d2aebd0e22024801bb183d3e119e46473ac0152a142103bc84ec393436b
+size 147311

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.8.0

ppo-LunarLander-v2/data

@@ -0,0 +1,96 @@
+{
+    "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 0x7f4b49a7ff70>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4b49a83040>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4b49a830d0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4b49a83160>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f4b49a831f0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f4b49a83280>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f4b49a83310>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4b49a833a0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f4b49a83430>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4b49a834c0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4b49a83550>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4b49a835e0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f4b49a82680>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1682018864876944371,
+    "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,
+    "_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 'gym.spaces.box.Box'>",
+        ":serialized:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAAAAAAAAAACUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAAAAAAAAAAAlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu",
+        "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,
+    "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:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/yZmZmZmZmoWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
+    },
+    "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:def27c572921161a56c8ffb6b9df8a8e66805233e3150d3336c539cfed404fbe
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b6176c58fbdbed56c893567463bf8effc354a3ce37e49abea7f8908e53ac760c
+size 43329

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: 1.8.0
+- PyTorch: 2.0.0+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 262.91548040984424, "std_reward": 20.587098994321945, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-20T19:50:29.038351"}