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: 252.77 +/- 23.77
+      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 0x7fafe7bbcdc0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fafe7bbce50>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fafe7bbcee0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fafe7bbcf70>", "_build": "<function ActorCriticPolicy._build at 0x7fafe7bc0040>", "forward": "<function ActorCriticPolicy.forward at 0x7fafe7bc00d0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fafe7bc0160>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fafe7bc01f0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fafe7bc0280>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fafe7bc0310>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fafe7bc03a0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fafe7bc0430>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fafe7bb7930>"}, "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": 1674152280471727753, "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-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "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 sha256:6bd003a2020be654286bd821372717126650f12b5fb13ef59f3ac23d3decfd6d
+size 147424

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

ppo-LunarLander-v2/data

@@ -0,0 +1,95 @@
+{
+    "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 0x7fafe7bbcdc0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fafe7bbce50>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fafe7bbcee0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fafe7bbcf70>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fafe7bc0040>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fafe7bc00d0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fafe7bc0160>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fafe7bc01f0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fafe7bc0280>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fafe7bc0310>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fafe7bc03a0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fafe7bc0430>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fafe7bb7930>"
+    },
+    "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": 1674152280471727753,
+    "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
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f1ddc813318825463f91aaa26ce050a1059a19fbd6308a4174c6f9ebd1e74526
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a47a22d667020fca2897bacb26d205772688452fb2035ebfb1992c9f2d6a45b5
+size 43393

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-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.21.6
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 252.76816071533344, "std_reward": 23.77498552259859, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-19T18:41:16.886305"}