Upload PPO LunarLander-v2 trained agent for 500k steps

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: 185.62 +/- 31.25
+      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 0x7f1d6b4d80e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1d6b4d8170>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1d6b4d8200>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1d6b4d8290>", "_build": "<function ActorCriticPolicy._build at 0x7f1d6b4d8320>", "forward": "<function ActorCriticPolicy.forward at 0x7f1d6b4d83b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1d6b4d8440>", "_predict": "<function ActorCriticPolicy._predict at 0x7f1d6b4d84d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1d6b4d8560>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1d6b4d85f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1d6b4d8680>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f1d6b51fa20>"}, "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": 507904, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1669751037599997058, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVwQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsBSwFLE0MEiABTAJROhZQpjAFflIWUjEgvdXNyL2xvY2FsL2xpYi9weXRob24zLjcvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuAQwIAAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEgvdXNyL2xvY2FsL2xpYi9weXRob24zLjcvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUdU5OaACMEF9tYWtlX2VtcHR5X2NlbGyUk5QpUpSFlHSUUpSMHGNsb3VkcGlja2xlLmNsb3VkcGlja2xlX2Zhc3SUjBJfZnVuY3Rpb25fc2V0c3RhdGWUk5RoH32UfZQoaBZoDYwMX19xdWFsbmFtZV9flIwZY29uc3RhbnRfZm4uPGxvY2Fscz4uZnVuY5SMD19fYW5ub3RhdGlvbnNfX5R9lIwOX19rd2RlZmF1bHRzX1+UTowMX19kZWZhdWx0c19flE6MCl9fbW9kdWxlX1+UaBeMB19fZG9jX1+UTowLX19jbG9zdXJlX1+UaACMCl9tYWtlX2NlbGyUk5RHPzOpKjBVMmGFlFKUhZSMF19jbG91ZHBpY2tsZV9zdWJtb2R1bGVzlF2UjAtfX2dsb2JhbHNfX5R9lHWGlIZSMC4="}, "_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": 124, "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-Ubuntu-18.04-bionic #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.7.15", "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 sha256:32632056484efb239da6716ee5b33e6113492b6bbb45056d35605438ed1b3cd4
+size 147146

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 0x7f1d6b4d80e0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1d6b4d8170>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1d6b4d8200>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1d6b4d8290>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f1d6b4d8320>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f1d6b4d83b0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1d6b4d8440>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f1d6b4d84d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1d6b4d8560>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1d6b4d85f0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1d6b4d8680>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f1d6b51fa20>"
+    },
+    "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": 507904,
+    "_total_timesteps": 500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1669751037599997058,
+    "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": 124,
+    "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 sha256:85f4deffd7dbf3468980c7978b4c44c562dd35a4dfd66d560c52095447eaa53e
+size 87865

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bbcfcb4e6ad804a4b27f2f378b3c17411561d41c9e96020051407766489d767a
+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-Ubuntu-18.04-bionic #1 SMP Fri Aug 26 08:44:51 UTC 2022
+Python: 3.7.15
+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": 185.61796641413116, "std_reward": 31.252502198839515, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-11-29T20:00:11.272606"}