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: 264.38 +/- 21.95
+      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 0x7fe8368ff160>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe8368ff1f0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe8368ff280>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe8368ff310>", "_build": "<function ActorCriticPolicy._build at 0x7fe8368ff3a0>", "forward": "<function ActorCriticPolicy.forward at 0x7fe8368ff430>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe8368ff4c0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fe8368ff550>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe8368ff5e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe8368ff670>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe8368ff700>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fe8368f95d0>"}, "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, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1670351856820593032, "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-5.10.133+-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": "1.12.1+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:35c2a78fa73a4092bd13adf64a5f8b33041d1fd28032d7f0377aa4eca2b30ca7
+size 147134

ppo-LunarLander-v2/_stable_baselines3_version

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

ppo-LunarLander-v2/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 0x7fe8368ff160>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe8368ff1f0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe8368ff280>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe8368ff310>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fe8368ff3a0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fe8368ff430>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe8368ff4c0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fe8368ff550>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe8368ff5e0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe8368ff670>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe8368ff700>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fe8368f95d0>"
+    },
+    "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,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1670351856820593032,
+    "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:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4BDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+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 sha256:98601fa9ffe727befda3e0cc9ee9b90810c7e64df177a5222da63f6898944691
+size 87865

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0ddd3866d69f237156f2c24f340f098c8f777c4d610d8330a6c52e76652e8308
+size 43201

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-5.10.133+-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: 1.12.1+cu113
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 264.3844010989689, "std_reward": 21.947012029155637, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-06T18:57:20.272750"}