Upload PPO LunarLander-v2 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: 238.70 +/- 34.64
+      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 0x7f5e1d324430>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5e1d3244c0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5e1d324550>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5e1d3245e0>", "_build": "<function ActorCriticPolicy._build at 0x7f5e1d324670>", "forward": "<function ActorCriticPolicy.forward at 0x7f5e1d324700>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f5e1d324790>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5e1d324820>", "_predict": "<function ActorCriticPolicy._predict at 0x7f5e1d3248b0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5e1d324940>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5e1d3249d0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5e1d324a60>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f5e1d320c00>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1690542698576602321, "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": 248, "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": 4, "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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"}, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.6", "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"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d3d5ca8daa6de0f6d296aa95cf246ba12539bcf02417cf921221b198440d95cb
+size 146745

ppo-LunarLander-v2/_stable_baselines3_version

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

ppo-LunarLander-v2/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 0x7f5e1d324430>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5e1d3244c0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5e1d324550>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5e1d3245e0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f5e1d324670>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f5e1d324700>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f5e1d324790>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5e1d324820>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f5e1d3248b0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5e1d324940>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5e1d3249d0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5e1d324a60>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f5e1d320c00>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1690542698576602321,
+    "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:": "gAWVPQwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQGfkky+HrQiMAWyUTegDjAF0lEdAmbMkwrUb1nV9lChoBkdAaAFYI0IkaGgHTegDaAhHQJmzhY8uBc11fZQoaAZHQGR3e67NB4VoB03oA2gIR0CZt5LNOdoWdX2UKGgGR0BkOs+qzZ6EaAdN6ANoCEdAmb1Lkjopx3V9lChoBkdAZQ4MqBmPHWgHTegDaAhHQJm+bsfJV811fZQoaAZHwDOR/kNnXd1oB0vaaAhHQJnBT90ihWZ1fZQoaAZHQGR2KArhBJJoB03oA2gIR0CZxJpeeFtbdX2UKGgGR0BgsfXbuc+aaAdN6ANoCEdAmcopmRNh3XV9lChoBkdAZV7cAR02cmgHTegDaAhHQJnLmi35N491fZQoaAZHQGHx1h9b5dpoB03oA2gIR0CZ0lTHbRF7dX2UKGgGR0BkngsGxD9gaAdN6ANoCEdAmdRPViF0xXV9lChoBkdAY9/6sySFG2gHTegDaAhHQJnt7wLE1l51fZQoaAZHQGQojneSB9VoB03oA2gIR0CZ8QD1GsmwdX2UKGgGR0Bns7pA2Q4kaAdN6ANoCEdAmffbobGWEHV9lChoBkdAZaNrAP/aQGgHTegDaAhHQJn8W/wiJO51fZQoaAZHQGM6HS4OMERoB03oA2gIR0CZ/qIcR15jdX2UKGgGR0Bhgy3AmAskaAdN6ANoCEdAmgRkaAFxGXV9lChoBkdAPFV+7UXpGGgHS9xoCEdAmgSuH8CPqHV9lChoBkdAZnIPUaya/mgHTegDaAhHQJoE6fK6nR91fZQoaAZHQGZx1yeZof1oB03oA2gIR0CaCPQzUI9ldX2UKGgGR0Bl1zx7RfF8aAdN6ANoCEdAmg3GZZ0Sy3V9lChoBkdAXwZQsPJ7s2gHTegDaAhHQJoOsqBmPHV1fZQoaAZHQGdp+kP+XJJoB03oA2gIR0CaEN1og3cYdX2UKGgGR0BkOTKkl/pdaAdN6ANoCEdAmhMsSkCV8nV9lChoBkdAYRGv9tMwlGgHTegDaAhHQJoYrFo+Ofd1fZQoaAZHQGFTTeXRgJFoB03oA2gIR0CaGiwAU+LWdX2UKGgGR0BoeLzZpSJkaAdN6ANoCEdAmiKQpz90inV9lChoBkdAYi8lTFVDKGgHTegDaAhHQJolyRq46Op1fZQoaAZHQGO6pAt4A0doB03oA2gIR0CaQh8e0XxfdX2UKGgGR0BjhSeqaPS2aAdN6ANoCEdAmkTPo7muDHV9lChoBkdAYTBa4+bExmgHTegDaAhHQJpPexZ+x4Z1fZQoaAZHQGPKOuA7PppoB03oA2gIR0CaUlazu4PPdX2UKGgGR0BerDUAksz3aAdN6ANoCEdAmlq16iTMaHV9lChoBkdAZkNHAh0QsmgHTegDaAhHQJpbMJY1YQt1fZQoaAZHQGTe2SEDhcZoB03oA2gIR0CaW4hTOxB3dX2UKGgGR0Bk8gDcM3IdaAdN6ANoCEdAmmFmWyC4BnV9lChoBkdAYZocSXdCV2gHTegDaAhHQJpmdzvJA+p1fZQoaAZHQGZcySNfgJloB03oA2gIR0CaZ2Q0GeMAdX2UKGgGR0BbGDvqkdmyaAdN6ANoCEdAmmmIV6/qPnV9lChoBkdAaBY0eEIw/WgHTegDaAhHQJpr+j/Mnqp1fZQoaAZHQGXKot+TeO5oB03oA2gIR0CachhBJI1+dX2UKGgGR0BgRUiSq2jPaAdN6ANoCEdAmnOw2Ifr8nV9lChoBkdAZKC2sJY1YWgHTegDaAhHQJp7VX/5tWN1fZQoaAZHQGMnxh+fAbhoB03oA2gIR0CafYVJtix3dX2UKGgGR0Bhuju6VdHEaAdN6ANoCEdAmoXjKPn0TXV9lChoBkdAY5F5/smfG2gHTegDaAhHQJqclybQTmJ1fZQoaAZHQG99zfrKNhpoB01oA2gIR0CannKJ2t+1dX2UKGgGR8AgGB19v0iAaAdL6GgIR0CaqYdY4hlldX2UKGgGR0Blm/0XgtOEaAdN6ANoCEdAmqmrFOwgT3V9lChoBkdAYoYYkVvddmgHTegDaAhHQJqwzw2ETQF1fZQoaAZHQGQI2pQ1rIpoB03oA2gIR0CasTUO/cnFdX2UKGgGR0Bi47nV5KODaAdN6ANoCEdAmrGAWSEDhnV9lChoBkdAYlkQ0XP7emgHTegDaAhHQJq2RqEeyRl1fZQoaAZHQGJa1dPci4doB03oA2gIR0CavAzqKP4mdX2UKGgGR0Bj9RtWMju8aAdN6ANoCEdAmr02KVII4XV9lChoBkfAKckGqxTsIGgHS8ZoCEdAmr8wgPmPo3V9lChoBkdAX87TVlPJrGgHTegDaAhHQJrAWGahHsl1fZQoaAZHQDfjxb0OEuhoB0vmaAhHQJrBJ0vGp/B1fZQoaAZHQGJgX7+DOC5oB03oA2gIR0Caw7cwxnFpdX2UKGgGR0Bhv/LaEi+taAdN6ANoCEdAmsoddu5z53V9lChoBkdAZFLN7jT8YWgHTegDaAhHQJrLpQsPJ7t1fZQoaAZHQGH3ojOcDr9oB03oA2gIR0Ca02gLJCBxdX2UKGgGR0Bivgs9SuQqaAdN6ANoCEdAmtXpwCKaX3V9lChoBkdAXVANNJvo/2gHTegDaAhHQJrhkPuogmt1fZQoaAZHQGQaSlWOp85oB03oA2gIR0Ca9svwVj7RdX2UKGgGR0BsqwKtxMnJaAdNIwNoCEdAmvzAnQY1pHV9lChoBkdAYA44JeE7GWgHTegDaAhHQJsDVb4agmJ1fZQoaAZHQGfapjlPrOZoB03oA2gIR0CbA3t2cJ+ldX2UKGgGR0BvYj0e2d/baAdN6QJoCEdAmwVKzAvcrXV9lChoBkdAX3XgAIY3vWgHTegDaAhHQJsQOHHmzSl1fZQoaAZHQCtOYc/+sHVoB0vraAhHQJsUCSU1Q691fZQoaAZHQF+SECvHLidoB03oA2gIR0CbFkTkhib2dX2UKGgGR0BjIeXu3MINaAdN6ANoCEdAmxkNtMwlB3V9lChoBkdAYHy73fyf+WgHTegDaAhHQJsZ/vBrN4Z1fZQoaAZHQGePXXqZ+hJoB03oA2gIR0CbGrejVQQ+dX2UKGgGR0BnACGJvYOEaAdN6ANoCEdAmxy+Vs1sL3V9lChoBkdAXGZh5Pdl/mgHTegDaAhHQJsjmmUGFBZ1fZQoaAZHQGfT77bcoH9oB03oA2gIR0CbJe76pHZsdX2UKGgGR0BEVZimVJL/aAdL52gIR0CbKihfShJzdX2UKGgGR0BjgABmwqy4aAdN6ANoCEdAmy/i2MKkVXV9lChoBkdAYhlCa7VawGgHTegDaAhHQJsyLIn0Cih1fZQoaAZHQGYQ5AhStNloB03oA2gIR0CbPS+vQnhLdX2UKGgGR0BlkH5WRzRyaAdN6ANoCEdAm1C+98JD3XV9lChoBkdAZFyEvCdjG2gHTegDaAhHQJtWgg4ffXR1fZQoaAZHQGEFdWZJCjVoB03oA2gIR0CbX8Mmnfl7dX2UKGgGR0BkZbpiZv1laAdN6ANoCEdAm2IRP420iXV9lChoBkdAZtSR7qptJmgHTegDaAhHQJtsTeDWbw11fZQoaAZHQGRkuxbB42VoB03oA2gIR0Cbb9csUZeidX2UKGgGR0Bf18pobn5jaAdN6ANoCEdAm3HQjlgc+HV9lChoBkdAQvDLbHp8nmgHTREBaAhHQJtyKkFfReF1fZQoaAZHQGZGoeHSF49oB03oA2gIR0CbdNnoxHoYdX2UKGgGR0Bg8+uFHrhSaAdN6ANoCEdAm3Vy/fwZwXV9lChoBkdAYEAfT1CgLGgHTegDaAhHQJt3Qdmxt551fZQoaAZHQGYFeokzGgloB03oA2gIR0CbfMOdGy5adX2UKGgGR0BmewVfu1F6aAdN6ANoCEdAm34+/UONHnV9lChoBkdAYseyB06o2mgHTegDaAhHQJuBRzhgmZ51fZQoaAZHQGH7Bh6Skj5oB03oA2gIR0CbhcFEiMYNdX2UKGgGR0BitzEtNBWxaAdN6ANoCEdAm4fsCDEm6XV9lChoBkdAXIZHI6r/82gHTegDaAhHQJuV/PUrkKh1fZQoaAZHQGGpQo9cKPZoB03oA2gIR0Cbl/TbnHNpdWUu"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 248,
+    "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": 4,
+    "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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"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:694cef551ed0959a92dd72d025120fc139a4403bb36d5aa442eb9c6a02b2c0aa
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c4a127b2213a5a0fc45c8fc173030d72c9c46f823474225b85e376519edefe30
+size 43329

ppo-LunarLander-v2/pytorch_variables.pth

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

ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
+- Python: 3.10.6
+- 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": 238.69941428710604, "std_reward": 34.64022795355703, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-28T12:05:17.571231"}