Upload PPO LunarLander-v2 trained agent

README.md

@@ -0,0 +1,36 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 169.43 +/- 77.42
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+---
+
+# **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 0x7f01cd8a75f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f01cd8a7680>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f01cd8a7710>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f01cd8a77a0>", "_build": "<function ActorCriticPolicy._build at 0x7f01cd8a7830>", "forward": "<function ActorCriticPolicy.forward at 0x7f01cd8a78c0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f01cd8a7950>", "_predict": "<function ActorCriticPolicy._predict at 0x7f01cd8a79e0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f01cd8a7a70>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f01cd8a7b00>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f01cd8a7b90>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f01cd8e7f90>"}, "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": 1663880491.2743607, "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:": "gAWVwQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsBSwFLE0MEiABTAJROhZQpjAFflIWUjEgvdXNyL2xvY2FsL2xpYi9weXRob24zLjcvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuAQwIAAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEgvdXNyL2xvY2FsL2xpYi9weXRob24zLjcvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUdU5OaACMEF9tYWtlX2VtcHR5X2NlbGyUk5QpUpSFlHSUUpSMHGNsb3VkcGlja2xlLmNsb3VkcGlja2xlX2Zhc3SUjBJfZnVuY3Rpb25fc2V0c3RhdGWUk5RoH32UfZQoaBZoDYwMX19xdWFsbmFtZV9flIwZY29uc3RhbnRfZm4uPGxvY2Fscz4uZnVuY5SMD19fYW5ub3RhdGlvbnNfX5R9lIwOX19rd2RlZmF1bHRzX1+UTowMX19kZWZhdWx0c19flE6MCl9fbW9kdWxlX1+UaBeMB19fZG9jX1+UTowLX19jbG9zdXJlX1+UaACMCl9tYWtlX2NlbGyUk5RHP8mZmZmZmZqFlFKUhZSMF19jbG91ZHBpY2tsZV9zdWJtb2R1bGVzlF2UjAtfX2dsb2JhbHNfX5R9lHWGlIZSMC4="}, "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": "3.7.14", "Stable-Baselines3": "1.6.0", "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 sha256:4ab2ecfe1849839e0b68000780c4f047c9639a8fa19051cbce5064c2ed9792f9
+size 147137

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.0

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 0x7f01cd8a75f0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f01cd8a7680>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f01cd8a7710>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f01cd8a77a0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f01cd8a7830>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f01cd8a78c0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f01cd8a7950>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f01cd8a79e0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f01cd8a7a70>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f01cd8a7b00>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f01cd8a7b90>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f01cd8e7f90>"
+    },
+    "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": 1663880491.2743607,
+    "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:": "gAWVwQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsBSwFLE0MEiABTAJROhZQpjAFflIWUjEgvdXNyL2xvY2FsL2xpYi9weXRob24zLjcvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuAQwIAAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEgvdXNyL2xvY2FsL2xpYi9weXRob24zLjcvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUdU5OaACMEF9tYWtlX2VtcHR5X2NlbGyUk5QpUpSFlHSUUpSMHGNsb3VkcGlja2xlLmNsb3VkcGlja2xlX2Zhc3SUjBJfZnVuY3Rpb25fc2V0c3RhdGWUk5RoH32UfZQoaBZoDYwMX19xdWFsbmFtZV9flIwZY29uc3RhbnRfZm4uPGxvY2Fscz4uZnVuY5SMD19fYW5ub3RhdGlvbnNfX5R9lIwOX19rd2RlZmF1bHRzX1+UTowMX19kZWZhdWx0c19flE6MCl9fbW9kdWxlX1+UaBeMB19fZG9jX1+UTowLX19jbG9zdXJlX1+UaACMCl9tYWtlX2NlbGyUk5RHP8mZmZmZmZqFlFKUhZSMF19jbG91ZHBpY2tsZV9zdWJtb2R1bGVzlF2UjAtfX2dsb2JhbHNfX5R9lHWGlIZSMC4="
+    },
+    "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 sha256:463f87c92e6b44070c1f7681addae85aa6fad3194835ff1303f08eb3f64fbe1d
+size 87865

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d3c2699fca513b04fe346940327d932eb6384fdde5cf518edb74afba03b06d26
+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: 3.7.14
+Stable-Baselines3: 1.6.0
+PyTorch: 1.12.1+cu113
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 169.42599114165105, "std_reward": 77.41796120938221, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-09-22T21:12:19.727192"}