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: 256.65 +/- 21.07
+      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 0x7aecfc0c9090>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7aecfc0c9120>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7aecfc0c91b0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7aecfc0c9240>", "_build": "<function ActorCriticPolicy._build at 0x7aecfc0c92d0>", "forward": "<function ActorCriticPolicy.forward at 0x7aecfc0c9360>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7aecfc0c93f0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7aecfc0c9480>", "_predict": "<function ActorCriticPolicy._predict at 0x7aecfc0c9510>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7aecfc0c95a0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7aecfc0c9630>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7aecfc0c96c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7aecfc068380>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1729000088055198303, "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:": "gAWVNwwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQHAIqeK8+RqMAWyUTQoBjAF0lEdAkTisYuTRpnV9lChoBkdAcgm9tMwlB2gHS/ZoCEdAkTjSUX531XV9lChoBkdAbc75bhWHUWgHTQEBaAhHQJE5I5WBBiV1fZQoaAZHQHCHA3o9s8BoB00CAWgIR0CROSj8UEgXdX2UKGgGR0BwcgZdfLLZaAdNDQFoCEdAkTmBLwnYx3V9lChoBkdAcwOQcPvrnmgHTQ4BaAhHQJE6OmwaBI51fZQoaAZHQHOYqMm4RVZoB0v7aAhHQJE6clKK5091fZQoaAZHQHGitKVY6n1oB00dAWgIR0CROvl8w5/9dX2UKGgGR0BxJ32f029+aAdNFwFoCEdAkTuky57PZHV9lChoBkdAcjqx2St/4WgHS/1oCEdAkTvOxwAEMnV9lChoBkdAcIGsA/9pAWgHTR4BaAhHQJE75pEhJRR1fZQoaAZHQHHUDmwJPZZoB00lAWgIR0CRPHWhysCDdX2UKGgGR0BzvSvdM0xeaAdL9mgIR0CRPVyRSxZ/dX2UKGgGR0BwuH9CNS62aAdNIQFoCEdAkT34smOU+3V9lChoBkdAcPChDgIhQmgHTR0BaAhHQJE+4kona391fZQoaAZHQHAViM5wOvtoB0vxaAhHQJE/Fn3+MqB1fZQoaAZHQHCC2C/XXiBoB00TAWgIR0CRP0dWQwK0dX2UKGgGR0BwxnHYHxBmaAdNFwFoCEdAkUCGCVbA13V9lChoBkdAcTiwH7gsLGgHTR0BaAhHQJFAr4VRDTl1fZQoaAZHQHG6+MQ2/BZoB00zAWgIR0CRQNokiUxEdX2UKGgGR0Bv4g4sEq2CaAdNJAFoCEdAkUFNZaFEiXV9lChoBkdAcBjfP5YYBWgHTScBaAhHQJFCf3dsSCh1fZQoaAZHQHAgE1l5GBpoB00TAWgIR0CRQ3My8BdVdX2UKGgGR0BxBgBeXzDoaAdNGQFoCEdAkUNzINmUW3V9lChoBkdAcJ4PHktEomgHTTYBaAhHQJFDmQGOdXl1fZQoaAZHQHArzVYp2EFoB01VAWgIR0CRQ66YVqN7dX2UKGgGR0BvgOgYgq3FaAdNJAFoCEdAkUP56dDpknV9lChoBkdAceoRjz7MxGgHTRYBaAhHQJFFIU0vXbx1fZQoaAZHQHJMKv3ai9JoB01AAWgIR0CRRTyYoiLVdX2UKGgGR0BwjbOjZcs2aAdL72gIR0CRRZRAKOT8dX2UKGgGR0BxSB71Iy0saAdNRgFoCEdAkUcH7xd6cHV9lChoBkdAce7VWS2Yv2gHTSoBaAhHQJFHok9lmOF1fZQoaAZHQHELg57w8W9oB0v/aAhHQJFH+EXcgyN1fZQoaAZHQHCTWbPQfIVoB01LAWgIR0CRSG1Gsmv4dX2UKGgGR0BVz5kf9xZMaAdLyWgIR0CRSOswco6TdX2UKGgGR0By0r+dbxEwaAdNLwFoCEdAkUkZQ1rIo3V9lChoBkdAcNDUd7v5QGgHTUEBaAhHQJFJwPpY9xJ1fZQoaAZHQHJ8/4AS39doB00+AWgIR0CRSkVRDTjOdX2UKGgGR0BsSdWOp84QaAdNGAFoCEdAkUtnOObRW3V9lChoBkdAcS02MKkVOGgHTSEBaAhHQJFLcHVwxWV1fZQoaAZHQHM8VlCkXUJoB01AAWgIR0CRS3fHggoxdX2UKGgGR0BxCsC7sfJWaAdNMQFoCEdAkUv5+tr9EXV9lChoBkdAbiunDziCKGgHTQABaAhHQJFMW7xusLh1fZQoaAZHQHK3LN8ma6VoB01AAWgIR0CRTNZ0jkdWdX2UKGgGR0BvOWfXf642aAdNIgFoCEdAkU1lDrqt5nV9lChoBkdAcCm6pYLb6GgHTTkBaAhHQJFOY/8l5W11fZQoaAZHQGvdlS88La5oB00XAWgIR0CRYlNCZ4OddX2UKGgGR0Bwc0j/uLJkaAdL/GgIR0CRYnFkhA4XdX2UKGgGR0BuUMbaRISUaAdNMwFoCEdAkWOw8jiXIHV9lChoBkdAbUnQOWjXWmgHTQ0BaAhHQJFjznNgSe11fZQoaAZHQHJ1I2CNCJJoB00sAWgIR0CRZDlpXZGsdX2UKGgGR0Bvt4t+TeO5aAdNFgFoCEdAkWQ44lyBCnV9lChoBkdAcBbpn6Eal2gHTTABaAhHQJFmKn+AEuB1fZQoaAZHQG2Ls3AEdNpoB00VAWgIR0CRZpfeUILPdX2UKGgGR0BwhQlAu7HyaAdNEQFoCEdAkWca3NLUTnV9lChoBkdAcidq59Vmz2gHTSkBaAhHQJFnLBj4Hop1fZQoaAZHQG0+6K+BYmtoB01JAWgIR0CRaCohIOH4dX2UKGgGR0ByxRj+aScLaAdNGQFoCEdAkWg/ReC04XV9lChoBkdAcJ+bDMvAXWgHTTUBaAhHQJFpsbcXWOJ1fZQoaAZHQHLhRqj8DSxoB00cAWgIR0CRaghaTwDvdX2UKGgGR0BuR5I6Kcd6aAdNEwFoCEdAkWpVPWQOnXV9lChoBkdAcM6y0a6z3WgHS/BoCEdAkWrV2icoY3V9lChoBkdAci0bYbsF+2gHTQUBaAhHQJFrUlb/wRZ1fZQoaAZHQHCjJElVtGdoB01VAWgIR0CRbGZs9B8hdX2UKGgGR0BuQvK8tf5UaAdNMQFoCEdAkW04geRxLnV9lChoBkdAcA/S0BwMpmgHTU4BaAhHQJFuHPfKp1l1fZQoaAZHQHMehoEjgQ9oB00JAmgIR0CRbuO8CgbqdX2UKGgGR0Bvpm8RL9MsaAdNFgFoCEdAkW9shC+lCXV9lChoBkdAcqKOTq0MPWgHTUgBaAhHQJFv8iA2AG11fZQoaAZHQHEtHuRcNYtoB00cAWgIR0CRcLOuaF23dX2UKGgGR0BxCyGJvYOEaAdNWAFoCEdAkXDUy+HrQnV9lChoBkdAcfEx7iQ1aWgHTSgBaAhHQJFw+Km8/Ux1fZQoaAZHQHIPV+y7f51oB0v6aAhHQJFxIfuCwr11fZQoaAZHQHIap+DvmYBoB01VAWgIR0CRcTPY4ACGdX2UKGgGR0BwZ1iCrcTKaAdNHQFoCEdAkXJiWzF+/nV9lChoBkdAcdu8cdYGMWgHTQUBaAhHQJFyeBFuvU11fZQoaAZHQHFJEgKWszVoB00fAWgIR0CRcq4ecQRPdX2UKGgGR0Bt+fBxgiNbaAdNKQFoCEdAkXPGOIZZS3V9lChoBkdAcsSeq7yxzWgHTRsBaAhHQJF0VzZHuqp1fZQoaAZHQHHPH+yZ8a5oB01HA2gIR0CRdPSNwR5DdX2UKGgGR0BxYC14Pf8/aAdL/2gIR0CRdROcDr7gdX2UKGgGR0Bxpx2HLzPKaAdNIAFoCEdAkXUuE7GNrHV9lChoBkdAcSZByjpLVWgHTQQBaAhHQJF14RK6Fuh1fZQoaAZHQG/3g8SwnploB00CAWgIR0CRdk+23KB/dX2UKGgGR0ByCamUGFBZaAdNNQFoCEdAkXgwdKdxyXV9lChoBkdAcIrKcd5prWgHTR4BaAhHQJF4R0hePaN1fZQoaAZHQHCH4WxhUipoB00TAWgIR0CReGaHbh3rdX2UKGgGR0BxVm2tuDSPaAdNJwFoCEdAkXjFLamGd3V9lChoBkdAb7vcry1/lWgHTS0BaAhHQJF4zNjbzsh1fZQoaAZHQHC3vNJOFg5oB00CAWgIR0CReT4PwuuidX2UKGgGR0BxvGN4qwyJaAdNMwFoCEdAkXsFpoK2KHV9lChoBkdAcjNuVHFxXGgHTUoBaAhHQJF7XuQZGax1fZQoaAZHQHDAvDYRNAVoB015AWgIR0CRe3cSGrS3dX2UKGgGR0ByZ4Xdj5KwaAdNIgFoCEdAkXvTe9Ba93V9lChoBkdAczSYsNDtxGgHTS4BaAhHQJF8xhoduHh1fZQoaAZHQHC7xZyMkyFoB00mAWgIR0CRfTgbZOBUdX2UKGgGR0Bt+j7bcoH+aAdNHgFoCEdAkX1AMMI/q3V9lChoBkdAc2CKmsNlRWgHS/VoCEdAkX1OcYqG13V9lChoBkdAcm/SV4X402gHTRYBaAhHQJF9wJrtVrB1fZQoaAZHQHIgnYtg8bJoB01JAWgIR0CRflFRYRukdWUu"}, "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:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=", "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-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.4.1+cu121", "GPU Enabled": "True", "Numpy": "1.26.4", "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 sha256:251c163abfea5fa35c072609d73f7735f912728b4d9e6efeadda19c2dfdcea36
+size 148068

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 0x7aecfc0c9090>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7aecfc0c9120>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7aecfc0c91b0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7aecfc0c9240>",
+        "_build": "<function ActorCriticPolicy._build at 0x7aecfc0c92d0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7aecfc0c9360>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7aecfc0c93f0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7aecfc0c9480>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7aecfc0c9510>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7aecfc0c95a0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7aecfc0c9630>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7aecfc0c96c0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7aecfc068380>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1729000088055198303,
+    "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:": "gAWVNwwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQHAIqeK8+RqMAWyUTQoBjAF0lEdAkTisYuTRpnV9lChoBkdAcgm9tMwlB2gHS/ZoCEdAkTjSUX531XV9lChoBkdAbc75bhWHUWgHTQEBaAhHQJE5I5WBBiV1fZQoaAZHQHCHA3o9s8BoB00CAWgIR0CROSj8UEgXdX2UKGgGR0BwcgZdfLLZaAdNDQFoCEdAkTmBLwnYx3V9lChoBkdAcwOQcPvrnmgHTQ4BaAhHQJE6OmwaBI51fZQoaAZHQHOYqMm4RVZoB0v7aAhHQJE6clKK5091fZQoaAZHQHGitKVY6n1oB00dAWgIR0CROvl8w5/9dX2UKGgGR0BxJ32f029+aAdNFwFoCEdAkTuky57PZHV9lChoBkdAcjqx2St/4WgHS/1oCEdAkTvOxwAEMnV9lChoBkdAcIGsA/9pAWgHTR4BaAhHQJE75pEhJRR1fZQoaAZHQHHUDmwJPZZoB00lAWgIR0CRPHWhysCDdX2UKGgGR0BzvSvdM0xeaAdL9mgIR0CRPVyRSxZ/dX2UKGgGR0BwuH9CNS62aAdNIQFoCEdAkT34smOU+3V9lChoBkdAcPChDgIhQmgHTR0BaAhHQJE+4kona391fZQoaAZHQHAViM5wOvtoB0vxaAhHQJE/Fn3+MqB1fZQoaAZHQHCC2C/XXiBoB00TAWgIR0CRP0dWQwK0dX2UKGgGR0BwxnHYHxBmaAdNFwFoCEdAkUCGCVbA13V9lChoBkdAcTiwH7gsLGgHTR0BaAhHQJFAr4VRDTl1fZQoaAZHQHG6+MQ2/BZoB00zAWgIR0CRQNokiUxEdX2UKGgGR0Bv4g4sEq2CaAdNJAFoCEdAkUFNZaFEiXV9lChoBkdAcBjfP5YYBWgHTScBaAhHQJFCf3dsSCh1fZQoaAZHQHAgE1l5GBpoB00TAWgIR0CRQ3My8BdVdX2UKGgGR0BxBgBeXzDoaAdNGQFoCEdAkUNzINmUW3V9lChoBkdAcJ4PHktEomgHTTYBaAhHQJFDmQGOdXl1fZQoaAZHQHArzVYp2EFoB01VAWgIR0CRQ66YVqN7dX2UKGgGR0BvgOgYgq3FaAdNJAFoCEdAkUP56dDpknV9lChoBkdAceoRjz7MxGgHTRYBaAhHQJFFIU0vXbx1fZQoaAZHQHJMKv3ai9JoB01AAWgIR0CRRTyYoiLVdX2UKGgGR0BwjbOjZcs2aAdL72gIR0CRRZRAKOT8dX2UKGgGR0BxSB71Iy0saAdNRgFoCEdAkUcH7xd6cHV9lChoBkdAce7VWS2Yv2gHTSoBaAhHQJFHok9lmOF1fZQoaAZHQHELg57w8W9oB0v/aAhHQJFH+EXcgyN1fZQoaAZHQHCTWbPQfIVoB01LAWgIR0CRSG1Gsmv4dX2UKGgGR0BVz5kf9xZMaAdLyWgIR0CRSOswco6TdX2UKGgGR0By0r+dbxEwaAdNLwFoCEdAkUkZQ1rIo3V9lChoBkdAcNDUd7v5QGgHTUEBaAhHQJFJwPpY9xJ1fZQoaAZHQHJ8/4AS39doB00+AWgIR0CRSkVRDTjOdX2UKGgGR0BsSdWOp84QaAdNGAFoCEdAkUtnOObRW3V9lChoBkdAcS02MKkVOGgHTSEBaAhHQJFLcHVwxWV1fZQoaAZHQHM8VlCkXUJoB01AAWgIR0CRS3fHggoxdX2UKGgGR0BxCsC7sfJWaAdNMQFoCEdAkUv5+tr9EXV9lChoBkdAbiunDziCKGgHTQABaAhHQJFMW7xusLh1fZQoaAZHQHK3LN8ma6VoB01AAWgIR0CRTNZ0jkdWdX2UKGgGR0BvOWfXf642aAdNIgFoCEdAkU1lDrqt5nV9lChoBkdAcCm6pYLb6GgHTTkBaAhHQJFOY/8l5W11fZQoaAZHQGvdlS88La5oB00XAWgIR0CRYlNCZ4OddX2UKGgGR0Bwc0j/uLJkaAdL/GgIR0CRYnFkhA4XdX2UKGgGR0BuUMbaRISUaAdNMwFoCEdAkWOw8jiXIHV9lChoBkdAbUnQOWjXWmgHTQ0BaAhHQJFjznNgSe11fZQoaAZHQHJ1I2CNCJJoB00sAWgIR0CRZDlpXZGsdX2UKGgGR0Bvt4t+TeO5aAdNFgFoCEdAkWQ44lyBCnV9lChoBkdAcBbpn6Eal2gHTTABaAhHQJFmKn+AEuB1fZQoaAZHQG2Ls3AEdNpoB00VAWgIR0CRZpfeUILPdX2UKGgGR0BwhQlAu7HyaAdNEQFoCEdAkWca3NLUTnV9lChoBkdAcidq59Vmz2gHTSkBaAhHQJFnLBj4Hop1fZQoaAZHQG0+6K+BYmtoB01JAWgIR0CRaCohIOH4dX2UKGgGR0ByxRj+aScLaAdNGQFoCEdAkWg/ReC04XV9lChoBkdAcJ+bDMvAXWgHTTUBaAhHQJFpsbcXWOJ1fZQoaAZHQHLhRqj8DSxoB00cAWgIR0CRaghaTwDvdX2UKGgGR0BuR5I6Kcd6aAdNEwFoCEdAkWpVPWQOnXV9lChoBkdAcM6y0a6z3WgHS/BoCEdAkWrV2icoY3V9lChoBkdAci0bYbsF+2gHTQUBaAhHQJFrUlb/wRZ1fZQoaAZHQHCjJElVtGdoB01VAWgIR0CRbGZs9B8hdX2UKGgGR0BuQvK8tf5UaAdNMQFoCEdAkW04geRxLnV9lChoBkdAcA/S0BwMpmgHTU4BaAhHQJFuHPfKp1l1fZQoaAZHQHMehoEjgQ9oB00JAmgIR0CRbuO8CgbqdX2UKGgGR0Bvpm8RL9MsaAdNFgFoCEdAkW9shC+lCXV9lChoBkdAcqKOTq0MPWgHTUgBaAhHQJFv8iA2AG11fZQoaAZHQHEtHuRcNYtoB00cAWgIR0CRcLOuaF23dX2UKGgGR0BxCyGJvYOEaAdNWAFoCEdAkXDUy+HrQnV9lChoBkdAcfEx7iQ1aWgHTSgBaAhHQJFw+Km8/Ux1fZQoaAZHQHIPV+y7f51oB0v6aAhHQJFxIfuCwr11fZQoaAZHQHIap+DvmYBoB01VAWgIR0CRcTPY4ACGdX2UKGgGR0BwZ1iCrcTKaAdNHQFoCEdAkXJiWzF+/nV9lChoBkdAcdu8cdYGMWgHTQUBaAhHQJFyeBFuvU11fZQoaAZHQHFJEgKWszVoB00fAWgIR0CRcq4ecQRPdX2UKGgGR0Bt+fBxgiNbaAdNKQFoCEdAkXPGOIZZS3V9lChoBkdAcsSeq7yxzWgHTRsBaAhHQJF0VzZHuqp1fZQoaAZHQHHPH+yZ8a5oB01HA2gIR0CRdPSNwR5DdX2UKGgGR0BxYC14Pf8/aAdL/2gIR0CRdROcDr7gdX2UKGgGR0Bxpx2HLzPKaAdNIAFoCEdAkXUuE7GNrHV9lChoBkdAcSZByjpLVWgHTQQBaAhHQJF14RK6Fuh1fZQoaAZHQG/3g8SwnploB00CAWgIR0CRdk+23KB/dX2UKGgGR0ByCamUGFBZaAdNNQFoCEdAkXgwdKdxyXV9lChoBkdAcIrKcd5prWgHTR4BaAhHQJF4R0hePaN1fZQoaAZHQHCH4WxhUipoB00TAWgIR0CReGaHbh3rdX2UKGgGR0BxVm2tuDSPaAdNJwFoCEdAkXjFLamGd3V9lChoBkdAb7vcry1/lWgHTS0BaAhHQJF4zNjbzsh1fZQoaAZHQHC3vNJOFg5oB00CAWgIR0CReT4PwuuidX2UKGgGR0BxvGN4qwyJaAdNMwFoCEdAkXsFpoK2KHV9lChoBkdAcjNuVHFxXGgHTUoBaAhHQJF7XuQZGax1fZQoaAZHQHDAvDYRNAVoB015AWgIR0CRe3cSGrS3dX2UKGgGR0ByZ4Xdj5KwaAdNIgFoCEdAkXvTe9Ba93V9lChoBkdAczSYsNDtxGgHTS4BaAhHQJF8xhoduHh1fZQoaAZHQHC7xZyMkyFoB00mAWgIR0CRfTgbZOBUdX2UKGgGR0Bt+j7bcoH+aAdNHgFoCEdAkX1AMMI/q3V9lChoBkdAc2CKmsNlRWgHS/VoCEdAkX1OcYqG13V9lChoBkdAcm/SV4X402gHTRYBaAhHQJF9wJrtVrB1fZQoaAZHQHIgnYtg8bJoB01JAWgIR0CRflFRYRukdWUu"
+    },
+    "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:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "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:a9d6d3c667f3801854c26142d95495f370e4c2088a6d53cf644f82b2b8b3f541
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bc52bf76f28f86f6b087a0c6e1b12a8ef389737cf1803b012f89c59b9e8f0f5e
+size 43762

ppo-LunarLander-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
+size 864

ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.4.1+cu121
+- GPU Enabled: True
+- Numpy: 1.26.4
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 256.6540640095473, "std_reward": 21.071178395853767, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-10-15T14:08:09.190098"}