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: 264.93 +/- 12.24
+      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 0x7f3e2044d670>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3e2044d700>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3e2044d790>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3e2044d820>", "_build": "<function ActorCriticPolicy._build at 0x7f3e2044d8b0>", "forward": "<function ActorCriticPolicy.forward at 0x7f3e2044d940>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f3e2044d9d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f3e2044da60>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f3e2044daf0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f3e2044db80>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f3e2044dc10>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f3e204c4ed0>"}, "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": 1670758776909986490, "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": "3.8.16", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+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 sha256:5db880203f5283b446c95c3bf036253d177068c053c8c601b843c4c0d0069497
+size 147214

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 0x7f3e2044d670>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3e2044d700>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3e2044d790>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3e2044d820>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f3e2044d8b0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f3e2044d940>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f3e2044d9d0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f3e2044da60>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f3e2044daf0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f3e2044db80>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f3e2044dc10>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f3e204c4ed0>"
+    },
+    "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": 1670758776909986490,
+    "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 sha256:8897414e3d3e7f0732e39bc1b6af2b9fe30974843174da6935cb314ce4900dd5
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:78fd0d54b17fad17f7e79b52f13fc79502b90e67fb3561da2efe2dcdfdfb2710
+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: 3.8.16
+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": 264.92761115767973, "std_reward": 12.237670781480785, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-11T12:02:05.798762"}