First attempt

First_attempt.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c2f4e601f5944c50d73882ff3be2c323867a3f46868a8adbb10b4a4d000fd65f
+size 147109

First_attempt/_stable_baselines3_version

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

First_attempt/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 0x7f6af532f6d0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6af532f760>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6af532f7f0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6af532f880>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f6af532f910>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f6af532f9a0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6af532fa30>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f6af532fac0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6af532fb50>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6af532fbe0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6af532fc70>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f6af5331640>"
+    },
+    "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": 114688,
+    "_total_timesteps": 100000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1671293163212866807,
+    "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": -0.1468799999999999,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 28,
+    "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
+}

First_attempt/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b42ec639542c510d69ef2ef19da2572fcddadd0baece58ff987925541185f1f0
+size 87929

First_attempt/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4fa3129dd9cd0cddd4241f48255ea45221cf73c850f31a2b4e2fbc4b7e929e4e
+size 43201

First_attempt/pytorch_variables.pth

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

First_attempt/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.35 #1 SMP Fri Apr 2 22:23:49 UTC 2021
+Python: 3.10.6
+Stable-Baselines3: 1.6.2
+PyTorch: 1.13.1+cu117
+GPU Enabled: True
+Numpy: 1.23.3
+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: -26.18 +/- 132.78
+      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 0x7f6af532f6d0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6af532f760>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6af532f7f0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6af532f880>", "_build": "<function ActorCriticPolicy._build at 0x7f6af532f910>", "forward": "<function ActorCriticPolicy.forward at 0x7f6af532f9a0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6af532fa30>", "_predict": "<function ActorCriticPolicy._predict at 0x7f6af532fac0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6af532fb50>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6af532fbe0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6af532fc70>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f6af5331640>"}, "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": 114688, "_total_timesteps": 100000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1671293163212866807, "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": -0.1468799999999999, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 28, "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-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.35 #1 SMP Fri Apr 2 22:23:49 UTC 2021", "Python": "3.10.6", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.1+cu117", "GPU Enabled": "True", "Numpy": "1.23.3", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -26.178356488667852, "std_reward": 132.77983760198262, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-17T18:07:50.430123"}