upload lander trained agent

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: 277.81 +/- 14.10
+      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
+
+...
+```

alex-lander1.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:805fd7e6611ced67cab18be2ef1338600ee12a73df2b076e6cfafed8f4e2f3d2
+size 146628

alex-lander1/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0a5

alex-lander1/data

@@ -0,0 +1,99 @@
+{
+    "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 0x79b66afc0310>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x79b66afc03a0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x79b66afc0430>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x79b66afc04c0>",
+        "_build": "<function ActorCriticPolicy._build at 0x79b66afc0550>",
+        "forward": "<function ActorCriticPolicy.forward at 0x79b66afc05e0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x79b66afc0670>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x79b66afc0700>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x79b66afc0790>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x79b66afc0820>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x79b66afc08b0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x79b66afc0940>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x79b66afb8e80>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1689382119365398161,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_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,
+    "_stats_window_size": 100,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 1550,
+    "observation_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True]",
+        "_shape": [
+            8
+        ],
+        "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "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": 25,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    }
+}

alex-lander1/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c692a7c317ebe7b3eb3d2db2cb9a2c50e3ebbc60578cff3a88d10d6433775b1a
+size 87929

alex-lander1/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d5bbfafb8b0b9e359ba14c95b90e60eb1ebf4f8ec5cec32ee1487cbb2a84fe4e
+size 43329

alex-lander1/pytorch_variables.pth

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

alex-lander1/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.109+-x86_64-with-glibc2.31 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

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 0x79b66afc0310>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x79b66afc03a0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x79b66afc0430>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x79b66afc04c0>", "_build": "<function ActorCriticPolicy._build at 0x79b66afc0550>", "forward": "<function ActorCriticPolicy.forward at 0x79b66afc05e0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x79b66afc0670>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x79b66afc0700>", "_predict": "<function ActorCriticPolicy._predict at 0x79b66afc0790>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x79b66afc0820>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x79b66afc08b0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x79b66afc0940>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x79b66afb8e80>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1689382119365398161, "learning_rate": 0.0003, "tensorboard_log": null, "_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, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWV4gsAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQHMgkTURWcWMAWyUS+2MAXSUR0Cqduk0zj3mdX2UKGgGR0BxnabMHKOlaAdLt2gIR0Cqdu6KDTScdX2UKGgGR0BOquQhfShKaAdLWWgIR0CqdykXDWK/dX2UKGgGR0BwjukCV8kVaAdLwmgIR0Cqd0IicG1QdX2UKGgGR0ByzvCN0eU7aAdL62gIR0Cqd4G/N7jUdX2UKGgGR0BxPsTlDF6zaAdL3mgIR0Cqd5qvV3EAdX2UKGgGR0BwMgTYdyT7aAdLr2gIR0Cqd+2dd3SsdX2UKGgGR0BypXNUwSJ1aAdLtmgIR0CqeBbihnJ1dX2UKGgGR0ByRSgezUqhaAdLzmgIR0CqeHMqBmPHdX2UKGgGR0BwRhn13+uOaAdLzWgIR0CqePPFvQ4TdX2UKGgGR0BwAb5qM3qBaAdLpWgIR0CqeUAxJul5dX2UKGgGR0Bx950EHMUzaAdL0GgIR0CqeVX1J17qdX2UKGgGR0By2H8VHnU2aAdLp2gIR0CqeYxCx/utdX2UKGgGR0BzS+xu89OiaAdL0GgIR0CqeZT0g8r7dX2UKGgGR0Bxa8lkYoAoaAdLpmgIR0CqeZOIqLCOdX2UKGgGR0Bx/WV4X40uaAdLsGgIR0CqeZz5wfhddX2UKGgGR0BzJbzasZHeaAdL5WgIR0CqecNcnmaIdX2UKGgGR0BuTsd7v5P/aAdLp2gIR0CqefIyCWeIdX2UKGgGR0ByqZ3t8eCDaAdLxGgIR0CqegHCwbEQdX2UKGgGR0Bwb0A3kxREaAdLuWgIR0Cqeh5SNwR5dX2UKGgGR0BwUxakhzNmaAdLwGgIR0CqsgG5DqnndX2UKGgGR0BxF7/EOy3TaAdLwGgIR0Cqshrz5GjLdX2UKGgGR0BwDljI7vG7aAdLvWgIR0Cqsmj5CWu6dX2UKGgGR0BzJmvdM0xeaAdLu2gIR0Cqsu8iwB5pdX2UKGgGR0ByV/7N0NjLaAdL4mgIR0CqszmY0EX+dX2UKGgGR0BxLwRAbADaaAdLpGgIR0Cqs+BDXvphdX2UKGgGR0BwNkdIXj2jaAdLtGgIR0Cqs/7C79Q5dX2UKGgGR0BuQAf6oESvaAdL0mgIR0CqtCTqB3A3dX2UKGgGR0BxPVqO938oaAdLwWgIR0CqtDCvX9R8dX2UKGgGR0Bxxzk2gnMMaAdL2GgIR0CqtFWqT8pDdX2UKGgGR0BxBtwdbPhRaAdLrWgIR0CqtFWd/axpdX2UKGgGR0BzGRiYsunNaAdL8mgIR0CqtGOh0yP/dX2UKGgGR0Bzc0+7lJYlaAdLz2gIR0CqtGaUzKs/dX2UKGgGR0ByFZirksBiaAdLwWgIR0CqtJZggHNYdX2UKGgGR0ByLzyd4FA3aAdL4WgIR0CqtLKFZgXudX2UKGgGR0BxgLrzGxUvaAdLxmgIR0CqtNMdcSoPdX2UKGgGR0BxKObvw3HaaAdLqmgIR0CqtNsj3VTadX2UKGgGR0BwHW5Etuk2aAdLnmgIR0CqtZBBZ6lddX2UKGgGR0BxePkS26TXaAdLwmgIR0CqtaYWDYh/dX2UKGgGR0Bza1OKwY+CaAdL2GgIR0CqtbP/aQFLdX2UKGgGR0Bwetr9ETg3aAdLz2gIR0CqtrQgLZzxdX2UKGgGR0BxBpqM3qA0aAdLumgIR0Cqtv13dKukdX2UKGgGR0BxSN1RtP56aAdLwWgIR0Cqtz+IVM24dX2UKGgGR0Bxoco5PuXvaAdLrmgIR0Cqt0yPluFYdX2UKGgGR0BzMiXw9aEBaAdLvmgIR0Cqt1FvQ4S6dX2UKGgGR0ByY/OPeYUnaAdLt2gIR0Cqt1uEdvKmdX2UKGgGR0ByGSvaDf3waAdLyWgIR0Cqt3l5GBnSdX2UKGgGR0Bzmu1lXiiqaAdLwmgIR0Cqt4Ye1a4ddX2UKGgGR0By7nDuSfUXaAdL5mgIR0Cqt+ZWilBQdX2UKGgGR0Byr9CJGe+VaAdL0GgIR0Cqt+qrJbMYdX2UKGgGR0Byys8OkLx7aAdL3WgIR0CquCj2zv7WdX2UKGgGR0Bw37cfvF3qaAdLq2gIR0CquC7N8ma6dX2UKGgGR0BzEEeeWfK7aAdLwGgIR0CquFwgs9SudX2UKGgGR0BxPehg3LmqaAdL7mgIR0CquGA3tKI0dX2UKGgGR0Bxiv0aqCHzaAdLxGgIR0CquHxdyDIzdX2UKGgGR0BwrCwW3z+WaAdLl2gIR0CquPsGHHmzdX2UKGgGR0BwLc/cFhXsaAdLtWgIR0CquSOLJjlQdX2UKGgGR0BzBi1uzhP1aAdNTQFoCEdAqrk8lzEJjXV9lChoBkdAcGmzeoDPnmgHS9doCEdAqrljDhtLtnV9lChoBkdAcdA86mwaBWgHS71oCEdAqrl7lLeyiXV9lChoBkdAcUs+iJwbVGgHS9RoCEdAqrmu2sq8UXV9lChoBkdAcW7NMXaakWgHS75oCEdAqrm0XUH6dnV9lChoBkdAcGWQ1aW5Y2gHS8xoCEdAqrmzO1OTJXV9lChoBkdAckbaWom5UmgHS8poCEdAqrnJAnlXBHV9lChoBkdAcxWqrBCUo2gHS7hoCEdAqrn101ZTynV9lChoBkdAb2N6be/HpGgHS6doCEdAqroF5OafBnV9lChoBkdAccvFt8/lhmgHS7doCEdAqroz1h9b5nV9lChoBkdAcccXlKbrkmgHS89oCEdAqro0TJyQxXV9lChoBkdAct9iWVu76GgHS81oCEdAqrqaJIlMRHV9lChoBkdAcYdTodMj/2gHS9JoCEdAqrrJWDHwPXV9lChoBkdAcQZgIyCWeGgHTQkBaAhHQKq7VRWtEG91fZQoaAZHQHJ0+CK77KtoB0vQaAhHQKq7fZ4fOlh1fZQoaAZHQHNBZSNwR5FoB0vgaAhHQKq7i4ku6Et1fZQoaAZHQHGQrkKeCkJoB0vPaAhHQKq7lgEU0vZ1fZQoaAZHQHA9U078vVVoB0utaAhHQKq7oQTVUdd1fZQoaAZHQHKcBTsIE8toB0uwaAhHQKq7w9f1Hvt1fZQoaAZHQHEUx9w3o9toB0vPaAhHQKq7zkn1Fph1fZQoaAZHQG6m8+RoysVoB0ujaAhHQKq701EVnEl1fZQoaAZHQHM1YPkJa7poB0vgaAhHQKq74+FlCkZ1fZQoaAZHQHIUa5sj3VVoB0vVaAhHQKq8DrAxi5N1fZQoaAZHQHH7G5+YtxxoB0vbaAhHQKq8GbBoEjh1fZQoaAZHQHJyp5Rjz7NoB0u4aAhHQKq8Rl05lvt1fZQoaAZHQHDVF9Brvb5oB0vPaAhHQKq8U03Ov+x1fZQoaAZHQHHmeTV2A5JoB0vZaAhHQKq8m35vcah1fZQoaAZHQHIx726ClJpoB0u6aAhHQKq83BC2MKl1fZQoaAZHQHMYpi3G4qhoB0vnaAhHQKq9KVlf7aZ1fZQoaAZHQHJD2qDK5kNoB0u0aAhHQKq9Rz/ZM+N1fZQoaAZHQHGPOlbeMydoB0uiaAhHQKq9SjxCpm51fZQoaAZHQHGHsoUi6hBoB0uXaAhHQKq9ZX7tRel1fZQoaAZHQHH42G/N7jVoB0uwaAhHQKq9ZuOS4e91fZQoaAZHQHHoEOmR/3FoB0ufaAhHQKq9dLpzLfV1fZQoaAZHQHBKvEsJ6Y5oB0u4aAhHQKq9jBRhttR1fZQoaAZHQHJofg75mAdoB0vjaAhHQKq95WHUMG51fZQoaAZHQHIO4m5UcXFoB0vKaAhHQKq9/zCDVYp1fZQoaAZHQHN+uGKyfL9oB0vCaAhHQKq+I606YE51fZQoaAZHQHF6yFoL5RFoB0u7aAhHQKq+UjrzGxV1fZQoaAZHQHFAvnnuAqdoB0vWaAhHQKq+Uq94/u91fZQoaAZHQHE5fWH1vl5oB0v7aAhHQKq+aCyyD7J1fZQoaAZHQHHIq0hNdqtoB0vIaAhHQKq+aInBtUJ1fZQoaAZHQEkHn5i3G4toB0t0aAhHQKq+gXBP9DR1ZS4="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 1550, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_shape": [8], "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "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": 25, "clip_range": {":type:": "<class 'function'>", ":serialized:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.31 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 277.81086890000006, "std_reward": 14.101431918744515, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-15T01:42:07.866112"}