first try unit 1!

LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3464c0aec8d421add24a42377c61e44704da936887483b40c47f0e90fe1ca112
+size 147332

LunarLander-v2/_stable_baselines3_version

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

LunarLander-v2/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 0x7fb04633b550>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fb04633b5e0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fb04633b670>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fb04633b700>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fb04633b790>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fb04633b820>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fb04633b8b0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fb04633b940>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fb04633b9d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fb04633ba60>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fb04633baf0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fb04633bb80>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fb04633d2c0>"
+    },
+    "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": 1678632537848528813,
+    "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
+}

LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:32806eb7366bc98d44862e29d98f798ba554ee40da372ffa8993471b8874b352
+size 87929

LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2004de37cdebd526a5bec6fc72fceaf62f1d87a08eece822e2697be5912b1bc9
+size 43393

LunarLander-v2/pytorch_variables.pth

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

LunarLander-v2/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.9.16
+- 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/policty_MlpPolicy/env_LunarLander-v2/lr_0.003/nsteps_2048/batchsize_64/nepochs_10/gamma_0.99/gae_0.95
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 267.17 +/- 22.88
+      name: mean_reward
+      verified: false
+---
+
+# **PPO/policty_MlpPolicy/env_LunarLander-v2/lr_0.003/nsteps_2048/batchsize_64/nepochs_10/gamma_0.99/gae_0.95** Agent playing **LunarLander-v2**
+This is a trained model of a **PPO/policty_MlpPolicy/env_LunarLander-v2/lr_0.003/nsteps_2048/batchsize_64/nepochs_10/gamma_0.99/gae_0.95** 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 0x7fb04633b550>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fb04633b5e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fb04633b670>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fb04633b700>", "_build": "<function ActorCriticPolicy._build at 0x7fb04633b790>", "forward": "<function ActorCriticPolicy.forward at 0x7fb04633b820>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fb04633b8b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fb04633b940>", "_predict": "<function ActorCriticPolicy._predict at 0x7fb04633b9d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fb04633ba60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fb04633baf0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fb04633bb80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fb04633d2c0>"}, "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": 1678632537848528813, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAOZ3LD4P5nO8Ln1Gu8ENhjnn0Nm9bn6EOgAAgD8AAIA/Ol1DPkKOkj/Q1BY/azUav4xXST670mg+AAAAAAAAAAAAy508XKNHuoK/ar0QnFs4f1gTurh2yLcAAIA/AACAP+Z/Hz60ORU+lXeuvWMITr5zAoY8goGKPAAAAAAAAAAAcwsjvo9MYLzq6wi8/iiPusbpxD17l2c7AACAPwAAgD+NMk8+SWoxPeHIQ75Q8aC9dwXQPFuB0bsAAAAAAAAAAFqXJj6cHHy8cAIku1oiWjkPwuK92KZZOgAAgD8AAIA/AK+hPRQ+orrLLFi9bAIINdMdITsW4na0AACAPwAAgD+NXw++kedKP8VB1b01+/m+7koLvvU42DwAAAAAAAAAAK1pcz4rtYM/Sn/CPhawDr+CBGQ+P+mEPQAAAAAAAAAAZv8Xvg/tA7xVXgK7hl89ucDBWT1gmh46AACAPwAAgD8z17E9UlCfuUWA57lH0Ke3ZLtXOrtiCTkAAAAAAAAAAPMuGL48TIM+IoIuPsIjvL7uHmc6K0yVPQAAAAAAAAAAehQkPkNUSbxDjnw690NzuNptqr17WqO5AACAPwAAgD9mkWW9XFsmuuN5BzhTWuQyUwbKuR4cGbcAAIA/AACAP8AqRD72il28OuoPu+71EjnA98K9Tp0xOgAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="}, "_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-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "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": 267.17168918888603, "std_reward": 22.881714071782042, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-12T15:41:09.343306"}