First Agent Ever

Apollo J.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:29d5bae0e06d7d884eeab9e25ccf0a313ee75300f4ab94ac93677909c2c4bd1a
+size 147416

Apollo J/_stable_baselines3_version

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

Apollo J/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 0x7f89603e8ca0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f89603e8d30>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f89603e8dc0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f89603e8e50>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f89603e8ee0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f89603e8f70>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f89603ed040>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f89603ed0d0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f89603ed160>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f89603ed1f0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f89603ed280>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f89603ed310>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f89603cac00>"
+    },
+    "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": 1677160023027832156,
+    "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
+}

Apollo J/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5e99fa49529273e7e310eff380b6703494e8244fe5c56997ce14cd4f8a980d5b
+size 87929

Apollo J/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:432a016bbc38e7979f358c24f3f3d1c35e8ee9712729522896bc8f19f6f149fc
+size 43393

Apollo J/pytorch_variables.pth

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

Apollo J/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: 1.22.4
+- 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: 252.05 +/- 23.51
+      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 0x7f89603e8ca0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f89603e8d30>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f89603e8dc0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f89603e8e50>", "_build": "<function ActorCriticPolicy._build at 0x7f89603e8ee0>", "forward": "<function ActorCriticPolicy.forward at 0x7f89603e8f70>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f89603ed040>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f89603ed0d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f89603ed160>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f89603ed1f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f89603ed280>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f89603ed310>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f89603cac00>"}, "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": 1677160023027832156, "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": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 252.05289109204946, "std_reward": 23.51007539855621, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-23T14:14:29.945163"}