upload ppo lunar lander 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: 277.87 +/- 22.43
+      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 0x7f60ba53dab0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f60ba53db40>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f60ba53dbd0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f60ba53dc60>", "_build": "<function ActorCriticPolicy._build at 0x7f60ba53dcf0>", "forward": "<function ActorCriticPolicy.forward at 0x7f60ba53dd80>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f60ba53de10>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f60ba53dea0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f60ba53df30>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f60ba53dfc0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f60ba53e050>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f60ba53e0e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f60ba536b00>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1685361148131803726, "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": 496, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "gAWVcAIAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWCAAAAAAAAAABAQEBAQEBAZRoB4wCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksIhZSMAUOUdJRSlIwNYm91bmRlZF9hYm92ZZRoECiWCAAAAAAAAAABAQEBAQEBAZRoFEsIhZRoGHSUUpSMBl9zaGFwZZRLCIWUjANsb3eUaBAoliAAAAAAAAAAAAC0wgAAtMIAAKDAAACgwNsPScAAAKDAAAAAgAAAAICUaApLCIWUaBh0lFKUjARoaWdolGgQKJYgAAAAAAAAAAAAtEIAALRCAACgQAAAoEDbD0lAAACgQAAAgD8AAIA/lGgKSwiFlGgYdJRSlIwIbG93X3JlcHKUjFtbLTkwLiAgICAgICAgLTkwLiAgICAgICAgIC01LiAgICAgICAgIC01LiAgICAgICAgIC0zLjE0MTU5MjcgIC01LgogIC0wLiAgICAgICAgIC0wLiAgICAgICBdlIwJaGlnaF9yZXBylIxTWzkwLiAgICAgICAgOTAuICAgICAgICAgNS4gICAgICAgICA1LiAgICAgICAgIDMuMTQxNTkyNyAgNS4KICAxLiAgICAgICAgIDEuICAgICAgIF2UjApfbnBfcmFuZG9tlE51Yi4=", "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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.11", "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 sha256:482ce660849ab3bb29917c7f9ff688b6aa10694e459e117c0d20d8130e96dca1
+size 146643

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 0x7f60ba53dab0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f60ba53db40>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f60ba53dbd0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f60ba53dc60>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f60ba53dcf0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f60ba53dd80>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f60ba53de10>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f60ba53dea0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f60ba53df30>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f60ba53dfc0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f60ba53e050>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f60ba53e0e0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f60ba536b00>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1685361148131803726,
+    "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:": "gAWV7gsAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQHDmlOO801uMAWyUS/yMAXSUR0Chp8nYHxBmdX2UKGgGR0BwTpqveP7vaAdL1mgIR0Chp+SydFvydX2UKGgGR0ByVA2sJY1YaAdL/WgIR0Chp/+0PYnOdX2UKGgGR0BzBnT3IuGsaAdL3WgIR0ChqCD8DSw4dX2UKGgGR0BydMu14Pf9aAdNRAFoCEdAoagqeAd4mnV9lChoBkdAcMdmMwUQCmgHS81oCEdAoag4msvIwXV9lChoBkdAciPbrC3w1GgHS8poCEdAoaiWGO+7DnV9lChoBkdAced+Y+jdpWgHTSgBaAhHQKGop8LKFIx1fZQoaAZHQHIwOZTho/RoB0vyaAhHQKGoqs/Y8Md1fZQoaAZHQHMYm0eEIxBoB0vIaAhHQKGor3X7LuB1fZQoaAZHQHC+jbeuV5doB0vhaAhHQKGowaWom5V1fZQoaAZHQHFgPlZHNHJoB0vjaAhHQKGqF4ZdfLN1fZQoaAZHQHFfDv3JxNtoB0vzaAhHQKGqUwudwvR1fZQoaAZHQHH7O+M6zVtoB0vaaAhHQKGqaPJ7sv91fZQoaAZHQHDXehsZYPpoB0vyaAhHQKGqodGRV6x1fZQoaAZHQHMqR+8XenBoB0vpaAhHQKGq2Ce2/i51fZQoaAZHQHBmjHS4OMFoB0vuaAhHQKGrMpEQXhx1fZQoaAZHQG97y/TLGJhoB0vWaAhHQKGrVffGdZt1fZQoaAZHQHEahxxT851oB00WAWgIR0Chq2WOp84QdX2UKGgGR0BxJz/82rGSaAdNBgFoCEdAoat8BZIQOHV9lChoBkdAcN0oDgZTAGgHS9RoCEdAoauLkhib2HV9lChoBkdAcdl3vhIe5mgHS+JoCEdAoauhjJ+2E3V9lChoBkdAcokFOfukUWgHS+1oCEdAoau9T72tdXV9lChoBkdAcsc4lyBClmgHS/xoCEdAoavp0W/JvHV9lChoBkdAbUKkWRA8jmgHTS0BaAhHQKGr73M6ikB1fZQoaAZHQHEXuez2OABoB0vRaAhHQKG2PXumaYx1fZQoaAZHQG9gltCRfWtoB0vlaAhHQKG2UA+6iCd1fZQoaAZHQHIOD2nKnvVoB0vuaAhHQKG2XVDKHO91fZQoaAZHQHBSQam4y45oB0v+aAhHQKG2XuLrHEN1fZQoaAZHQHOcIAGSpzdoB0vzaAhHQKG3ML+glGB1fZQoaAZHQHAbxhhH9WJoB0vfaAhHQKG3RgLJCBx1fZQoaAZHQHCmLy6MBIZoB0vUaAhHQKG3T83uNPx1fZQoaAZHQHB+x6Skj5doB0vnaAhHQKG30PxQSBd1fZQoaAZHQHCAUg4ffXRoB0vhaAhHQKG3zXOnl4l1fZQoaAZHQHL5/rB0p3JoB00MA2gIR0Cht81RtP56dX2UKGgGR0ByMZ82Jiy6aAdL3WgIR0ChuEzMRpUQdX2UKGgGR0BzHuH+IdlvaAdL7mgIR0ChuGKTbFjvdX2UKGgGR0BxNNs/IKc/aAdL3mgIR0ChuGKYRdyDdX2UKGgGR0Bw7LpfQa73aAdL92gIR0ChuGXpnpSrdX2UKGgGR0ByQlkxyn1naAdNEwFoCEdAobiB8rqdH3V9lChoBkdAb9byhi9ZimgHTXoCaAhHQKG5IPy08eV1fZQoaAZHQHOZ2KVII4VoB0vNaAhHQKG6JSR8twt1fZQoaAZHQHDTCtV7x/doB0vZaAhHQKG6XdDYywh1fZQoaAZHQHK84H9m6GxoB0vfaAhHQKG6X4Vymyh1fZQoaAZHQHI0+m3vx6RoB0vYaAhHQKG7OU7CBPN1fZQoaAZHQHGX8YIjW09oB00RAWgIR0Chu2GmLtNSdX2UKGgGR0BvaHqX4TK1aAdLymgIR0Chu3KfvnbJdX2UKGgGR0Bxwfch1TzeaAdL7GgIR0Chu4hg3LmqdX2UKGgGR0ByP7YvnKW+aAdL2WgIR0Chu7n3UQTVdX2UKGgGR0ByqpL+PzWgaAdL32gIR0Chu9rwvxpddX2UKGgGR0Bw1WsS00FbaAdL02gIR0ChvA7Gm1pkdX2UKGgGR0Bw1HokiUxEaAdL0mgIR0ChvBlFtsN2dX2UKGgGR0BuVKZ2IO6NaAdL1WgIR0ChvCWtU4rCdX2UKGgGR0ByCNwBHTZyaAdL22gIR0ChvD/lZHNHdX2UKGgGR0Bw4o84gieNaAdL5mgIR0ChvIFZX+2mdX2UKGgGR0ByfnLQokRjaAdNLQFoCEdAobyg5DJEIHV9lChoBkdAcYtpsGgSOGgHS/RoCEdAob0IxpL26HV9lChoBkdAcAsqZML4OGgHS95oCEdAob2VcD8tPHV9lChoBkdAcbJDbJwKjWgHS+5oCEdAob3JAQg9vHV9lChoBkdAc4cQm/nGKmgHS/loCEdAob3DaGpMpXV9lChoBkdAcZ0yfL9uP2gHS9hoCEdAob4Y2ycCo3V9lChoBkdAcfOyqMm4RWgHS91oCEdAob5Co60Y0nV9lChoBkdAcYYSYw7DEWgHS+VoCEdAob5mkzoECHV9lChoBkdAct175Ec81WgHS+BoCEdAob6gU+LWJHV9lChoBkdAc0bcinpB5WgHS+loCEdAob6mucMEzXV9lChoBkdAbqQ8IzFdcGgHS/poCEdAob68H4XXRXV9lChoBkdASLNpEhJRO2gHS75oCEdAob7blA/s3XV9lChoBkdAcUrnXd0q6WgHS+toCEdAob724G2TgXV9lChoBkdAb9huogmqpGgHS/VoCEdAob8Lpqynk3V9lChoBkdAcWgVR1oxpWgHS/NoCEdAob8eTPjXF3V9lChoBkdAcNkuJ1q33GgHS+hoCEdAob8wNwzch3V9lChoBkdAcQrT4tYjjmgHTRMBaAhHQKG/VGS6lLx1fZQoaAZHQHAXx6fJ3gVoB0vfaAhHQKG/nNfPX051fZQoaAZHQHFnQfuCwr1oB0vVaAhHQKHACIZZSvV1fZQoaAZHQHLAuqrBCUpoB0vXaAhHQKHAPxri2lV1fZQoaAZHQHEq5bY9Pk9oB0vnaAhHQKHAa3Kji4t1fZQoaAZHQHF7Dh99c8loB0vxaAhHQKHA47Njbzt1fZQoaAZHQHO/cZYPoV5oB0vYaAhHQKHA4NmUW2x1fZQoaAZHQHOenA6+36RoB0vJaAhHQKHA73g1m8N1fZQoaAZHQHBLGhqTKT1oB0vTaAhHQKHBCYIBzWB1fZQoaAZHQHC6udkJ8fFoB0vQaAhHQKHBGWNWEK51fZQoaAZHQHBZ163RXwNoB00FAWgIR0ChwUVNQCSzdX2UKGgGR0BxW9yZKFqSaAdL42gIR0ChwWyHmA9WdX2UKGgGR0BzUP3mFJxvaAdL2WgIR0ChwX9uP3i8dX2UKGgGR0BwxQuUUwi8aAdL0mgIR0ChwZEBCD28dX2UKGgGR0BzYAOx0MgEaAdL72gIR0Chwc55qubJdX2UKGgGR0By+Yo+fRNRaAdL7GgIR0ChwgEuQIUrdX2UKGgGR0Bxa9AxBVuKaAdL1mgIR0Chwo2Rq46PdX2UKGgGR0Bycm6nR9gGaAdNAwFoCEdAocKjidat93V9lChoBkfAMHJDmbLEDWgHS3doCEdAocLLWNFSbnV9lChoBkdAcDQ9uP3i72gHS9RoCEdAocLuys0YTHV9lChoBkdAcr501IiC8WgHS9hoCEdAocNvUjLSu3V9lChoBkdAc4U5le4TbmgHS89oCEdAocONZA6dUnV9lChoBkdAccwirT6SDGgHS+ZoCEdAocOfwy6+WXV9lChoBkdAcmzGvfTCtWgHS+VoCEdAocOsLtu1nnV9lChoBkdAcJhbVSXMQmgHS+xoCEdAocPf47A+IXV9lChoBkdAbpOwMYuTR2gHS9toCEdAocPtRDTjN3V9lChoBkdAcQHfYSQHRmgHTTIBaAhHQKHD/VlwtJ51fZQoaAZHQG/VgJTl1bJoB0vMaAhHQKHD/XRPXTV1fZQoaAZHQHCGyzPa+N9oB0vraAhHQKHEchTOxB51ZS4="
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 496,
+    "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 sha256:7cf5adfc3883dfeda23e64e0c9007689774084ca89091bf10b518411e88ef572
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6d5377b2fe42b70ec3d7f16997841b0b104900ff43a553120099e51bc5a8990b
+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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023
+- Python: 3.10.11
+- 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": 277.8742805215494, "std_reward": 22.42762368921572, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-05-29T12:09:18.049653"}