Upload PPO LunarLander-v2 trained agent 1,5M 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: 274.84 +/- 24.62
+      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 0x7f05b65b1ca0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f05b65b1d30>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f05b65b1dc0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f05b65b1e50>", "_build": "<function ActorCriticPolicy._build at 0x7f05b65b1ee0>", "forward": "<function ActorCriticPolicy.forward at 0x7f05b65b1f70>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f05b65b7040>", "_predict": "<function ActorCriticPolicy._predict at 0x7f05b65b70d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f05b65b7160>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f05b65b71f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f05b65b7280>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f05b662e480>"}, "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": 1, "num_timesteps": 1500160, "_total_timesteps": 1500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1672171979536374635, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAHptVb53DgY/PSasPqBTj76oAUg9ykeRPgAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.00010666666666669933, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 9768, "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 sha256:03b94885a749af4660e8e7ee237b022c8f7a3636e0df6e2632cd0ae8f59c26b8
+size 146448

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 0x7f05b65b1ca0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f05b65b1d30>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f05b65b1dc0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f05b65b1e50>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f05b65b1ee0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f05b65b1f70>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f05b65b7040>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f05b65b70d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f05b65b7160>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f05b65b71f0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f05b65b7280>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f05b662e480>"
+    },
+    "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": 1,
+    "num_timesteps": 1500160,
+    "_total_timesteps": 1500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1672171979536374635,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAHptVb53DgY/PSasPqBTj76oAUg9ykeRPgAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.00010666666666669933,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 9768,
+    "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 sha256:741c3398ecfe6e4a86aa2e77ac795ace3a07f02fd92742229c471cff2c057f78
+size 87929

ppo-Lunarlander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:00561bd621cffd37dd7090704701de6bdd2c90af5a22f7e381b253852f26b00d
+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": 274.83813239608367, "std_reward": 24.62326328681975, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-27T21:05:09.610421"}