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: 276.14 +/- 9.55
+      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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7fe4423e8430>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe4423e84c0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe4423e8550>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe4423e85e0>", "_build": "<function ActorCriticPolicy._build at 0x7fe4423e8670>", "forward": "<function ActorCriticPolicy.forward at 0x7fe4423e8700>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe4423e8790>", "_predict": "<function ActorCriticPolicy._predict at 0x7fe4423e8820>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe4423e88b0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe4423e8940>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe4423e89d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fe4423e61e0>"}, "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": 1507328, "_total_timesteps": 1500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1670502645704038153, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4BDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+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": -0.004885333333333408, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 460, "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": 5, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.15", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo-LunarLander-v2-3.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:934039a963539abb458bc880e387447b45c41f090310d00955e86925ed866246
+size 147102

ppo-LunarLander-v2-3/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.2

ppo-LunarLander-v2-3/data

@@ -0,0 +1,94 @@
+{
+    "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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7fe4423e8430>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe4423e84c0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe4423e8550>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe4423e85e0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fe4423e8670>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fe4423e8700>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe4423e8790>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fe4423e8820>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe4423e88b0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe4423e8940>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe4423e89d0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fe4423e61e0>"
+    },
+    "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": 1507328,
+    "_total_timesteps": 1500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1670502645704038153,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAHMKSj436qw/bRUZP7no/r7LVdE+eqw1PgAAAAAAAAAAZl4VPODgPD+2Qr499KTavmmI8rxkNsE9AAAAAAAAAACaaI48Mfu5PzK11z43ANs+OSL9uxlwvDwAAAAAAAAAAJoMgb2PHkC6aE3UMrmzejAIzzQ6yH6iswAAgD8AAIA/M6E4PgeJMT9DbcE9Fm3jvpXbRD46/QO+AAAAAAAAAACAKFU9D6l2P3eIpT2OudS+rHU3vRaHGj0AAAAAAAAAAJoxpjukMDW5y3/AvaGhkTY1VgO824MGtgAAAAAAAAAAzaPevE09mz8AI9S9XorxvkYW172a4DK+AAAAAAAAAAAz0Z08y1KSPXhxULupTJW+kn6SPVLHFjwAAAAAAAAAAE20XT7e4YQ/y170Pho5K7+TgKc+0We9PQAAAAAAAAAATeM9vc24YT9awvw9J3z3vhL+t71HSAU+AAAAAAAAAACAiGE9uCrpPDtv5r0yTVW+XuKGPeBcWLwAAAAAAAAAAM2TJr1/2Q4+KPd5PlH9t75Voic+7tO+vQAAAAAAAAAAANSuO1xzGrpihJiy/FJcsLqfWjvKXxUzAACAPwAAgD8a/pM9ohQ8PgHeH75MVJu+fkzFPFxeojwAAAAAAAAAAA1yjT3oq+E90oeDvs9fdr64L3m9MLFlPQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": -0.004885333333333408,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 460,
+    "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": 5,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

ppo-LunarLander-v2-3/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:82d6b60e38fb9ec58fe76787485d8e695224bee14481344db64297bb4207b4ed
+size 87929

ppo-LunarLander-v2-3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0464a4e226ae8cabb37797fafd164af758cdecedb220d35f9294cfb123307ba2
+size 43201

ppo-LunarLander-v2-3/pytorch_variables.pth

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

ppo-LunarLander-v2-3/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
+Python: 3.8.15
+Stable-Baselines3: 1.6.2
+PyTorch: 1.13.0+cu116
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 276.1362948969285, "std_reward": 9.550924079105428, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-08T12:57:11.059543"}