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: 263.01 +/- 16.60
+      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 0x7f83d19391f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f83d1939280>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f83d1939310>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f83d19393a0>", "_build": "<function ActorCriticPolicy._build at 0x7f83d1939430>", "forward": "<function ActorCriticPolicy.forward at 0x7f83d19394c0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f83d1939550>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f83d19395e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f83d1939670>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f83d1939700>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f83d1939790>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f83d1939820>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f83d1936f00>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1682092728282524431, "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:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAAB2BD2P4nu6Agovuem9HrSIGSi7BrdMOAAAgD8AAIA/7cQwPqFkqLwHLUE77+PQuRL4Fr4uabi6AACAPwAAgD/NzDa7e2KnumJih7lUX4C0vmI8OYZ2mzgAAIA/AACAP7P1QD1Is4i6fQjwOlwUxjWYBE2666oLugAAgD8AAAAAZih1Pa5Vmrrq3Nm6Ea3ItQBaiLo6Rfs5AACAPwAAgD8a/1k+f2tGPltUY74T3Te+k3OQPa6TmbwAAAAAAAAAAFrR+r3hL5Y/ttpBv35eI79xzcu8IjcQvgAAAAAAAAAAzbuEPNenUrt4WP677HiEPKaGeTxunmS9AACAPwAAgD8aZHY94SCRutJTtjeJppUyapk6ubhV07YAAIA/AACAP42Qu73UAgY/gqhvPcMTsL7TN1K9+MiOPAAAAAAAAAAAZnpRPtCl5T5RTiG+vPJfvvsMGLxGSrK9AAAAAAAAAAAatbM9MvGlPyOX7D5kYgK/kLzRPeX+Qz4AAAAAAAAAAI1b7T3nc4A+6wMmvmRaXL6+fIQ96w7XPAAAAAAAAAAA048SvntHyj7exUs+1dugvix2aLwJVrI9AAAAAAAAAABmEIk9H5W2ueNPdLker/wzk5XyuyZRkDgAAIA/AAAAAJrUqTx7VoO6xssnvqJNcTj9g7A7xlbftwAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="}, "_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 sha256:2757b7cf1662c6dd1322ddfb62db8d91f0415bddf94c4f110cb0767ba5664dab
+size 147391

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 0x7f83d19391f0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f83d1939280>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f83d1939310>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f83d19393a0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f83d1939430>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f83d19394c0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f83d1939550>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f83d19395e0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f83d1939670>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f83d1939700>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f83d1939790>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f83d1939820>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f83d1936f00>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1682092728282524431,
+    "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
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:32286b5119660e2d276086a4ebc96ef53dd6bf12c0192e9757173604edf80242
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:18086abc751fe97b8953be9c255419b1ff71b1fb781d3d6c4610eb88b0027f98
+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": 263.01412338156626, "std_reward": 16.598797527459197, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-21T16:40:51.742059"}