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: 244.42 +/- 15.27
+      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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7fe12bd2d670>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe12bd2d700>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe12bd2d790>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe12bd2d820>", "_build": "<function ActorCriticPolicy._build at 0x7fe12bd2d8b0>", "forward": "<function ActorCriticPolicy.forward at 0x7fe12bd2d940>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe12bd2d9d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fe12bd2da60>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe12bd2daf0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe12bd2db80>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe12bd2dc10>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fe12bd2b2d0>"}, "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": 1670788096149091710, "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:": "gAWVgBAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMI7Uj1nV+tXkCUhpRSlIwBbJRN6AOMAXSUR0CQ8u9QoCuEdX2UKGgGaAloD0MIqiheZW2+X0CUhpRSlGgVTegDaBZHQJD0Fyfcvdx1fZQoaAZoCWgPQwiQT8jOW9xkQJSGlFKUaBVN6ANoFkdAkQkk1Q66rnV9lChoBmgJaA9DCA02dR6VbWVAlIaUUpRoFU3oA2gWR0CRC/PhQ3xXdX2UKGgGaAloD0MIEFmkifcEZ0CUhpRSlGgVTegDaBZHQJEOoLE1l5J1fZQoaAZoCWgPQwjYfjLGB1NjQJSGlFKUaBVN6ANoFkdAkQ6kTHsC1nV9lChoBmgJaA9DCAh2/BeIUWdAlIaUUpRoFU3oA2gWR0CREPyZrpJPdX2UKGgGaAloD0MIB5j5Dn5QZkCUhpRSlGgVTegDaBZHQJEUWQhfShJ1fZQoaAZoCWgPQwgDQBU37j1lQJSGlFKUaBVN6ANoFkdAkRWpYDDCQHV9lChoBmgJaA9DCF/rUiN0H2dAlIaUUpRoFU3oA2gWR0CRGIPj4pMIdX2UKGgGaAloD0MIc51GWqpiYkCUhpRSlGgVTegDaBZHQJEgRSGahHt1fZQoaAZoCWgPQwgbR6zFJ3hkQJSGlFKUaBVN6ANoFkdAkSBtdiUgS3V9lChoBmgJaA9DCJD3qpUJHGZAlIaUUpRoFU3oA2gWR0CRIaEt/WlNdX2UKGgGaAloD0MIfsnGg63DZkCUhpRSlGgVTegDaBZHQJEjE3kxREZ1fZQoaAZoCWgPQwjBqnr5HQJnQJSGlFKUaBVN6ANoFkdAkSkS/sVtXXV9lChoBmgJaA9DCCR9WkX/C2VAlIaUUpRoFU3oA2gWR0CRPbzFMqSYdX2UKGgGaAloD0MINlzknq6BZECUhpRSlGgVTegDaBZHQJE/7OgQHzJ1fZQoaAZoCWgPQwiDNc6mI+FjQJSGlFKUaBVN6ANoFkdAkUDHfZVXFXV9lChoBmgJaA9DCOeLvRdf02JAlIaUUpRoFU3oA2gWR0CRUVmHxjJ/dX2UKGgGaAloD0MIe4fboWHaZ0CUhpRSlGgVTegDaBZHQJFTrGbTc7B1fZQoaAZoCWgPQwjfap24HHFhQJSGlFKUaBVN6ANoFkdAkVXn9m6GxnV9lChoBmgJaA9DCEFHq1pSaWJAlIaUUpRoFU3oA2gWR0CRVerOZ9eAdX2UKGgGaAloD0MI/+ibNI2CZECUhpRSlGgVTegDaBZHQJFYNvOyE+R1fZQoaAZoCWgPQwiSkh6GVjVkQJSGlFKUaBVN6ANoFkdAkVwdUjs2N3V9lChoBmgJaA9DCAiu8gTCYWBAlIaUUpRoFU3oA2gWR0CRXd92ovSMdX2UKGgGaAloD0MI5ZzYQ/uuZECUhpRSlGgVTegDaBZHQJFh4py6tkp1fZQoaAZoCWgPQwgbvoV1YzBnQJSGlFKUaBVN6ANoFkdAkWwmIwdsBXV9lChoBmgJaA9DCOBNt+yQuWRAlIaUUpRoFU3oA2gWR0CRbE003wTedX2UKGgGaAloD0MIHOp3YWsrY0CUhpRSlGgVTegDaBZHQJFtXGaQV9F1fZQoaAZoCWgPQwgxRE5fTyFjQJSGlFKUaBVN6ANoFkdAkW7QL/jsEHV9lChoBmgJaA9DCDi+9swSDWlAlIaUUpRoFU3oA2gWR0CRdMl9BrvcdX2UKGgGaAloD0MIDTfg80NkYECUhpRSlGgVTegDaBZHQJF2H7fpD/l1fZQoaAZoCWgPQwiKrDWUWoVkQJSGlFKUaBVN6ANoFkdAkYzUFB6a9nV9lChoBmgJaA9DCACL/Poh4WZAlIaUUpRoFU3oA2gWR0CRjfC+10DEdX2UKGgGaAloD0MI/5dr0QKEY0CUhpRSlGgVTegDaBZHQJGiDBSDRMN1fZQoaAZoCWgPQwhcHJWbqCVdQJSGlFKUaBVN6ANoFkdAkaTvyoXKsHV9lChoBmgJaA9DCII65dENlGRAlIaUUpRoFU3oA2gWR0CRp7A1ejVQdX2UKGgGaAloD0MIUwQ4vYvoXUCUhpRSlGgVTegDaBZHQJGntHd43WF1fZQoaAZoCWgPQwhOl8XE5idgQJSGlFKUaBVN6ANoFkdAkapMtoSL63V9lChoBmgJaA9DCJrpXif1iWRAlIaUUpRoFU3oA2gWR0CRrkE+xGDudX2UKGgGaAloD0MIUz2Zf3TVZECUhpRSlGgVTegDaBZHQJGvvmITGo91fZQoaAZoCWgPQwg6B8+EJo1dQJSGlFKUaBVN6ANoFkdAkbLybx3FDXV9lChoBmgJaA9DCGL1RxiGZmVAlIaUUpRoFU3oA2gWR0CRu72LpA2RdX2UKGgGaAloD0MIo68gzVhCaUCUhpRSlGgVTegDaBZHQJG77vXsgMd1fZQoaAZoCWgPQwiTAaCKGwlLQJSGlFKUaBVNAQFoFkdAkb0BKg7HQ3V9lChoBmgJaA9DCLjmjv6X7V5AlIaUUpRoFU3oA2gWR0CRvVNNJvpAdX2UKGgGaAloD0MI8UV7vJBsZUCUhpRSlGgVTegDaBZHQJG+0xnFo+R1fZQoaAZoCWgPQwgd5PVg0sZkQJSGlFKUaBVN6ANoFkdAkcS1/DtPYXV9lChoBmgJaA9DCIOhDitca2NAlIaUUpRoFU3oA2gWR0CRxe8+A3DOdX2UKGgGaAloD0MIecxAZXyiZkCUhpRSlGgVTegDaBZHQJHbUoTfzjF1fZQoaAZoCWgPQwgqU8xB0NZmQJSGlFKUaBVN6ANoFkdAkdwoYR/ViHV9lChoBmgJaA9DCBVzEHQ0YGNAlIaUUpRoFU3oA2gWR0CR7LrhR64UdX2UKGgGaAloD0MIjgWFQRkpZkCUhpRSlGgVTegDaBZHQJHvHc0tRN11fZQoaAZoCWgPQwgt0VlmEUZkQJSGlFKUaBVN6ANoFkdAkfFzcRDkVHV9lChoBmgJaA9DCK9d2nBYy2NAlIaUUpRoFU3oA2gWR0CR8XXGwRoRdX2UKGgGaAloD0MIHZCEfbuXY0CUhpRSlGgVTegDaBZHQJHzqVB2Ohl1fZQoaAZoCWgPQwiCWDZzyFVjQJSGlFKUaBVN6ANoFkdAkfhf69CeE3V9lChoBmgJaA9DCAithy+TA2dAlIaUUpRoFU3oA2gWR0CR+17L+xW1dX2UKGgGaAloD0MIgPChREtPZECUhpRSlGgVTegDaBZHQJIDHX6InBt1fZQoaAZoCWgPQwjs98Q6Ve1cQJSGlFKUaBVN6ANoFkdAkgNNUjs2N3V9lChoBmgJaA9DCKd38X7cc2ZAlIaUUpRoFU3oA2gWR0CSBC8vEjxDdX2UKGgGaAloD0MIY7fPKjMbZECUhpRSlGgVTegDaBZHQJIEbv2GqPx1fZQoaAZoCWgPQwgAqOLGLahmQJSGlFKUaBVN6ANoFkdAkgXcUqQRw3V9lChoBmgJaA9DCPF+3H75s2ZAlIaUUpRoFU3oA2gWR0CSC+aYeDFqdX2UKGgGaAloD0MIn1p9dVVhYECUhpRSlGgVTegDaBZHQJINMPGyX2N1fZQoaAZoCWgPQwjKoxth0cBhQJSGlFKUaBVN6ANoFkdAkg+RG6PKdXV9lChoBmgJaA9DCFu21hcJC2NAlIaUUpRoFU3oA2gWR0CSI6ZA6dUbdX2UKGgGaAloD0MIYobGE0HZYECUhpRSlGgVTegDaBZHQJI18Qrc0tR1fZQoaAZoCWgPQwgYRKSm3fBgQJSGlFKUaBVN6ANoFkdAkjiwjQiRn3V9lChoBmgJaA9DCAsOL4hIb2RAlIaUUpRoFU3oA2gWR0CSO21sLv1EdX2UKGgGaAloD0MIQrEVNK1MYECUhpRSlGgVTegDaBZHQJI7cDlo11p1fZQoaAZoCWgPQwiafLPNDTpiQJSGlFKUaBVN6ANoFkdAkj4g7DEWI3V9lChoBmgJaA9DCARz9Pi9Z15AlIaUUpRoFU3oA2gWR0CSQ5IxxkupdX2UKGgGaAloD0MIeZEJ+DXnY0CUhpRSlGgVTegDaBZHQJJG9RHf/FR1fZQoaAZoCWgPQwibPdAKDChkQJSGlFKUaBVN6ANoFkdAkk+xTS9dvHV9lChoBmgJaA9DCMIXJlMFWWNAlIaUUpRoFU3oA2gWR0CST95Ec81XdX2UKGgGaAloD0MIqODwgoghaECUhpRSlGgVTegDaBZHQJJQ0bzbvgF1fZQoaAZoCWgPQwjZJaq3hhdhQJSGlFKUaBVN6ANoFkdAklEWeHzpYHV9lChoBmgJaA9DCLJmZJA7AWFAlIaUUpRoFU3oA2gWR0CSUn8HfMwDdX2UKGgGaAloD0MI+aI9XkgDX0CUhpRSlGgVTegDaBZHQJJYXzVc2R91fZQoaAZoCWgPQwjVeOkmsTBhQJSGlFKUaBVN6ANoFkdAklmvEfkmyHV9lChoBmgJaA9DCA0Zj1IJi2dAlIaUUpRoFU3oA2gWR0CSW/n2qT8pdX2UKGgGaAloD0MIEMmQY2ujYUCUhpRSlGgVTegDaBZHQJJc8p7TlT51fZQoaAZoCWgPQwg2y2WjczNjQJSGlFKUaBVN6ANoFkdAkoL4R28qWnV9lChoBmgJaA9DCF+0xwvpSXBAlIaUUpRoFU0oAmgWR0CSg620AtFsdX2UKGgGaAloD0MIw2M/i6XwZUCUhpRSlGgVTegDaBZHQJKFW5TZQHl1fZQoaAZoCWgPQwhpG3+isq5iQJSGlFKUaBVN6ANoFkdAkoeFsUIsy3V9lChoBmgJaA9DCLq6Y7FNaGJAlIaUUpRoFU3oA2gWR0CSh4ijL0SRdX2UKGgGaAloD0MI0v9yLdp3YUCUhpRSlGgVTegDaBZHQJKJr27FsHl1fZQoaAZoCWgPQwhcyvlib6xuQJSGlFKUaBVNhwJoFkdAkooPFzdUKnV9lChoBmgJaA9DCGoSvCGNQFxAlIaUUpRoFU3oA2gWR0CSjf1n/T9bdX2UKGgGaAloD0MIuaXVkLgWY0CUhpRSlGgVTegDaBZHQJKQ49GI9DB1fZQoaAZoCWgPQwgZVvFGZsJiQJSGlFKUaBVN6ANoFkdAkplPtx+8XnV9lChoBmgJaA9DCMSXiSKkKmFAlIaUUpRoFU3oA2gWR0CSmXwaisXBdX2UKGgGaAloD0MIuK6YEd5XZECUhpRSlGgVTegDaBZHQJKagY77sOZ1fZQoaAZoCWgPQwhj00ohEHFmQJSGlFKUaBVN6ANoFkdAkqQz8tPHk3V9lChoBmgJaA9DCMeEmEsqzmFAlIaUUpRoFU3oA2gWR0CSpcR+z+m4dX2UKGgGaAloD0MILo7KTdQaOUCUhpRSlGgVS89oFkdAkqaSVObiInV9lChoBmgJaA9DCLmNBvAWs2NAlIaUUpRoFU3oA2gWR0CSqGnTAnD0dX2UKGgGaAloD0MIB7ZKsLgeZUCUhpRSlGgVTegDaBZHQJKpcN3GGVR1ZS4="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "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, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6a1c8719d24dcb6cc1d8131472458aa9c272253f8893f4ccf55d6f46d30ac6a5
+size 147218

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.2

ppo-LunarLander-v2/data

@@ -0,0 +1,94 @@
+{
+    "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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7fe12bd2d670>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe12bd2d700>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe12bd2d790>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe12bd2d820>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fe12bd2d8b0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fe12bd2d940>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe12bd2d9d0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fe12bd2da60>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe12bd2daf0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe12bd2db80>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe12bd2dc10>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fe12bd2b2d0>"
+    },
+    "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": 1670788096149091710,
+    "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": 248,
+    "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
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e781aa20d3a5d026c7389b3a3d25ad890b27a93620d7afe66962a78318c838b3
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:29af228b9cdfa48f21836015ee1f06ad41d2f04b0b246f4f0ef146d65e953624
+size 43201

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 +1,7 @@
+OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
+Python: 3.8.16
+Stable-Baselines3: 1.6.2
+PyTorch: 1.13.0+cu116
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 244.41979524019922, "std_reward": 15.272858453123108, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-11T20:15:52.637434"}