Push LunarLander-v2

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: 250.25 +/- 34.78
+      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 0x7f89744ca430>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f89744ca4c0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f89744ca550>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f89744ca5e0>", "_build": "<function ActorCriticPolicy._build at 0x7f89744ca670>", "forward": "<function ActorCriticPolicy.forward at 0x7f89744ca700>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f89744ca790>", "_predict": "<function ActorCriticPolicy._predict at 0x7f89744ca820>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f89744ca8b0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f89744ca940>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f89744ca9d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f89744c4960>"}, "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": 1671981258043020808, "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-lunar-lander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:19d2f865b39d2ea43330fae5fed18af1c553304db382b3c3df32053ac0b9b4ee
+size 147210

ppo-lunar-lander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.2

ppo-lunar-lander-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 0x7f89744ca430>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f89744ca4c0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f89744ca550>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f89744ca5e0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f89744ca670>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f89744ca700>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f89744ca790>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f89744ca820>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f89744ca8b0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f89744ca940>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f89744ca9d0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f89744c4960>"
+    },
+    "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": 1671981258043020808,
+    "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-lunar-lander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:50e15753e3dbbfe0f978d38d68b8ffe3dd2a30ed4b4d575f7a3e477698d3c1d4
+size 87929

ppo-lunar-lander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c61c866d148a0b72f11f5a6bceb6f284403ebd2f2345256917e7a33fc3a00fe6
+size 43201

ppo-lunar-lander-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

ppo-lunar-lander-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": 250.24769192344974, "std_reward": 34.779565757108436, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-25T15:40:25.593738"}