Journey starts

MlpPolicy_lander.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d5afe1f9fe0751603c991749bc128f2e1a1979b01169d86be086eb57e77ec62b
+size 147137

MlpPolicy_lander/_stable_baselines3_version

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

MlpPolicy_lander/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 0x7fbf2a7a65e0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbf2a7a6670>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbf2a7a6700>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbf2a7a6790>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fbf2a7a6820>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fbf2a7a68b0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbf2a7a6940>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fbf2a7a69d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbf2a7a6a60>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbf2a7a6af0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbf2a7a6b80>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fbf2a81ee10>"
+    },
+    "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": 1670696344296973236,
+    "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": 310,
+    "n_steps": 2048,
+    "gamma": 0.99,
+    "gae_lambda": 0.95,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 10,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

MlpPolicy_lander/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b1d5881eb0129f33fb32f471dfbbfced9b445d5477286667e8ac1585060e1dd3
+size 87929

MlpPolicy_lander/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5209d438706a0244202d9d430c0a14a5510846fddd4d5ac2c198f75015390141
+size 43201

MlpPolicy_lander/pytorch_variables.pth

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

MlpPolicy_lander/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

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: 259.90 +/- 18.83
+      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 0x7fbf2a7a65e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbf2a7a6670>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbf2a7a6700>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbf2a7a6790>", "_build": "<function ActorCriticPolicy._build at 0x7fbf2a7a6820>", "forward": "<function ActorCriticPolicy.forward at 0x7fbf2a7a68b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbf2a7a6940>", "_predict": "<function ActorCriticPolicy._predict at 0x7fbf2a7a69d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbf2a7a6a60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbf2a7a6af0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbf2a7a6b80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fbf2a81ee10>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAAAAAAAAAACUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAAAAAAAAAAAlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu", "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": 1670696344296973236, "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": 310, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "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"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 259.9009495373393, "std_reward": 18.82753022986291, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-10T19:07:29.198963"}