Upload PPO based LunarLander-v2 Agent trained with MLP Policy for 100M steps

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: 302.99 +/- 20.23
+      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 0x7f124846b820>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f124846b8b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f124846b940>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f124846b9d0>", "_build": "<function ActorCriticPolicy._build at 0x7f124846ba60>", "forward": "<function ActorCriticPolicy.forward at 0x7f124846baf0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f124846bb80>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f124846bc10>", "_predict": "<function ActorCriticPolicy._predict at 0x7f124846bca0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f124846bd30>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f124846bdc0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f124846be50>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f124846c680>"}, "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": 1, "num_timesteps": 100007936, "_total_timesteps": 100000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1679616981410490396, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": null, "_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": -7.935999999997279e-05, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVHRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMInplgOFdYckCUhpRSlIwBbJRLkowBdJRHQPF51AfJV811fZQoaAZoCWgPQwjezOhHg7JzQJSGlFKUaBVLuWgWR0DxedRXU6PsdX2UKGgGaAloD0MISx5Pyw+YcECUhpRSlGgVS5toFkdA8Xoehx1gY3V9lChoBmgJaA9DCENwXMZNrXFAlIaUUpRoFUukaBZHQPF6H29i+cp1fZQoaAZoCWgPQwgcBvNXiLhyQJSGlFKUaBVLtmgWR0DxeiA+X7cgdX2UKGgGaAloD0MI+Wncm1+6cUCUhpRSlGgVS6VoFkdA8XogXUMG5nV9lChoBmgJaA9DCJRnXg57xXFAlIaUUpRoFUuUaBZHQPF6I9LRKHx1fZQoaAZoCWgPQwg57Sk5J91yQJSGlFKUaBVLsGgWR0DxeiRtPHktdX2UKGgGaAloD0MIuycPCzXwc0CUhpRSlGgVS8FoFkdA8XokY3WFvnV9lChoBmgJaA9DCAQ3UrYIM3FAlIaUUpRoFUuVaBZHQPF6Jb0voNd1fZQoaAZoCWgPQwhcyCO4kUdzQJSGlFKUaBVLxmgWR0DxeiYWTX8PdX2UKGgGaAloD0MINPPkmgLOckCUhpRSlGgVS7xoFkdA8XomgdKdx3V9lChoBmgJaA9DCGQFvw1xknFAlIaUUpRoFUuZaBZHQPF6J0Jmdy11fZQoaAZoCWgPQwg5tp4hHBNyQJSGlFKUaBVLqWgWR0DxeioePq9odX2UKGgGaAloD0MIBKkUO1quckCUhpRSlGgVS5poFkdA8Xoq9JnQIHV9lChoBmgJaA9DCFMkXwnkZXBAlIaUUpRoFUuXaBZHQPF6KvQD3dt1fZQoaAZoCWgPQwgVxausralzQJSGlFKUaBVLs2gWR0Dxeiy2vStvdX2UKGgGaAloD0MIkNeDSTEbc0CUhpRSlGgVS7doFkdA8Xos5r+HanV9lChoBmgJaA9DCNy5MNILGXBAlIaUUpRoFUubaBZHQPF6Lull9Sd1fZQoaAZoCWgPQwgnF2Ng3axwQJSGlFKUaBVLoWgWR0DxejBbX6IndX2UKGgGaAloD0MIMgIqHEEBckCUhpRSlGgVS55oFkdA8Xow53xFzHV9lChoBmgJaA9DCMeA7PUusXFAlIaUUpRoFUuhaBZHQPF6MR9/jKh1fZQoaAZoCWgPQwiIDRZO0hJwQJSGlFKUaBVLlGgWR0DxejUCUHIIdX2UKGgGaAloD0MIaObJNYWBckCUhpRSlGgVS6toFkdA8Xo2Oqebu3V9lChoBmgJaA9DCB2Txf0HxHJAlIaUUpRoFUuwaBZHQPF6NuYc/+t1fZQoaAZoCWgPQwjGi4UhMv1zQJSGlFKUaBVLumgWR0Dxejdgy/KydX2UKGgGaAloD0MIcyoZACptb0CUhpRSlGgVS6NoFkdA8Xo3iGi5/nV9lChoBmgJaA9DCMi3dw26xnBAlIaUUpRoFUukaBZHQPF6OFXPqs51fZQoaAZoCWgPQwiE8j6OJtpxQJSGlFKUaBVLsWgWR0DxejiU0vXcdX2UKGgGaAloD0MIIlUUr7JzcECUhpRSlGgVS5loFkdA8Xo60dvKl3V9lChoBmgJaA9DCGMraFqiwXFAlIaUUpRoFUupaBZHQPF6O6zLOiZ1fZQoaAZoCWgPQwj1DrdDAx9xQJSGlFKUaBVLm2gWR0Dxej0BDG96dX2UKGgGaAloD0MIeuQPBp62ckCUhpRSlGgVS7NoFkdA8Xo9gSOBD3V9lChoBmgJaA9DCERq2sV0W3FAlIaUUpRoFUuqaBZHQPF6PmACnxd1fZQoaAZoCWgPQwiVLCeh9KpxQJSGlFKUaBVLhmgWR0Dxej8qhDgJdX2UKGgGaAloD0MIpdqn47EicUCUhpRSlGgVS5NoFkdA8Xo/wwj+rHV9lChoBmgJaA9DCEuS5/o+MnNAlIaUUpRoFUuaaBZHQPF6QPPppvh1fZQoaAZoCWgPQwgQ5+EEZvhyQJSGlFKUaBVLuWgWR0DxekIg2qDLdX2UKGgGaAloD0MItoZSe5FVckCUhpRSlGgVS4doFkdA8XpEM63iJnV9lChoBmgJaA9DCG03wTcNpXBAlIaUUpRoFUuYaBZHQPF6RPDm8ul1fZQoaAZoCWgPQwjvN9pxQ5JzQJSGlFKUaBVLmmgWR0Dxekii5NGmdX2UKGgGaAloD0MIJVgcznxQckCUhpRSlGgVS6loFkdA8XpJbeVLSXV9lChoBmgJaA9DCGyYofHEyHNAlIaUUpRoFUuwaBZHQPF6Sg6PsAx1fZQoaAZoCWgPQwi6TbhXZp9zQJSGlFKUaBVLvmgWR0Dxeksk43m3dX2UKGgGaAloD0MIKIBiZAmQckCUhpRSlGgVS7VoFkdA8XpL6d1+zHV9lChoBmgJaA9DCPXb14GzPXFAlIaUUpRoFUuiaBZHQPF6TUhX8wZ1fZQoaAZoCWgPQwiNQ/0ubG9wQJSGlFKUaBVLtGgWR0Dxek5O1v2odX2UKGgGaAloD0MIa39ne7QacECUhpRSlGgVS6VoFkdA8XpPe6iCa3V9lChoBmgJaA9DCA6CjlY1UnNAlIaUUpRoFUuzaBZHQPF6UHUgB911fZQoaAZoCWgPQwhJgnAF1DZyQJSGlFKUaBVLlmgWR0DxelGFcpsodX2UKGgGaAloD0MI+S6lLlnucUCUhpRSlGgVS4toFkdA8XpRmZNO/XV9lChoBmgJaA9DCHnMQGX80HNAlIaUUpRoFUuvaBZHQPF6UjzOHFh1fZQoaAZoCWgPQwjmdi/3SVF0QJSGlFKUaBVLwmgWR0DxelNUp/gBdX2UKGgGaAloD0MIhugQOJIPc0CUhpRSlGgVS7poFkdA8XpT5fICEHV9lChoBmgJaA9DCJGYoIZvUHBAlIaUUpRoFUuaaBZHQPF6VXkbPyF1fZQoaAZoCWgPQwjsaYe/5opzQJSGlFKUaBVLrGgWR0Dxelam51/2dX2UKGgGaAloD0MIQfFjzJ15ckCUhpRSlGgVS4poFkdA8XpYWknCwnV9lChoBmgJaA9DCJAy4gJQTW9AlIaUUpRoFUuYaBZHQPF6WTuJDVp1fZQoaAZoCWgPQwiVnuklBntzQJSGlFKUaBVLo2gWR0DxelmptaZAdX2UKGgGaAloD0MIdm7ajFNrckCUhpRSlGgVS6loFkdA8XpddwBHTnV9lChoBmgJaA9DCGd+NQeI6nNAlIaUUpRoFUu3aBZHQPF6Xkj2SMd1fZQoaAZoCWgPQwi45LhTevNyQJSGlFKUaBVLtWgWR0DxemAjSofkdX2UKGgGaAloD0MIryMO2QBLckCUhpRSlGgVS7RoFkdA8XphEKNQ03V9lChoBmgJaA9DCGu7Cb4pJ3RAlIaUUpRoFUuzaBZHQPF6YiU8mrt1fZQoaAZoCWgPQwiD9urjoWxvQJSGlFKUaBVLlmgWR0DxemMDrJKbdX2UKGgGaAloD0MI/U0oREAxckCUhpRSlGgVS6xoFkdA8XpjaoMrmXV9lChoBmgJaA9DCGE1lrB2yHFAlIaUUpRoFUusaBZHQPF6Y4IiTt91fZQoaAZoCWgPQwgkJqjhWzxzQJSGlFKUaBVLuGgWR0DxemOiXpnpdX2UKGgGaAloD0MIKnReY9dAckCUhpRSlGgVS65oFkdA8XpkTYh+v3V9lChoBmgJaA9DCOS+1TqxvXJAlIaUUpRoFUuuaBZHQPF6Zd6MR6F1fZQoaAZoCWgPQwi7YHDN3TJyQJSGlFKUaBVLgWgWR0DxemX29crzdX2UKGgGaAloD0MIaRmp9xTAckCUhpRSlGgVS7JoFkdA8Xpn8CYCyXV9lChoBmgJaA9DCJc8npafWXBAlIaUUpRoFUuwaBZHQPF6aPQfIS11fZQoaAZoCWgPQwg9u3zrgytyQJSGlFKUaBVLnmgWR0DxemnAkLQYdX2UKGgGaAloD0MIBYnt7kGsc0CUhpRSlGgVS6BoFkdA8XpqYkAxSHV9lChoBmgJaA9DCBAf2PFfGm9AlIaUUpRoFUuIaBZHQPF6bhLBbfR1fZQoaAZoCWgPQwhb0HtjSKRzQJSGlFKUaBVLu2gWR0DxenEXOGCadX2UKGgGaAloD0MIXHLcKd1Qc0CUhpRSlGgVS5JoFkdA8XpxTG96C3V9lChoBmgJaA9DCJlH/mAghnNAlIaUUpRoFUu7aBZHQPF6ceU9pyp1fZQoaAZoCWgPQwjwT6kS5XpxQJSGlFKUaBVLp2gWR0DxenJ8nNPhdX2UKGgGaAloD0MIw0Xu6SoGckCUhpRSlGgVS5hoFkdA8Xpy2hM8HXV9lChoBmgJaA9DCBu4A3VKLXNAlIaUUpRoFUuVaBZHQPF6cvMbFS91fZQoaAZoCWgPQwgX1/hM9idyQJSGlFKUaBVLqGgWR0DxenT9Q40edX2UKGgGaAloD0MIHt5zYDmpc0CUhpRSlGgVS69oFkdA8Xp1kv4/NnV9lChoBmgJaA9DCL8OnDOiV3NAlIaUUpRoFUu2aBZHQPF6dw9r4351fZQoaAZoCWgPQwiLNPEO8J1zQJSGlFKUaBVLvGgWR0Dxenl1tO2zdX2UKGgGaAloD0MIH4MVpxo6cUCUhpRSlGgVS59oFkdA8Xp5j/6wdXV9lChoBmgJaA9DCGU1XU80ZXNAlIaUUpRoFUuuaBZHQPF6eh6F/QV1fZQoaAZoCWgPQwjA7QkSG2RyQJSGlFKUaBVLxGgWR0Dxeno+rELqdX2UKGgGaAloD0MIDkxuFBkJckCUhpRSlGgVS6JoFkdA8Xp6qMzdlHV9lChoBmgJaA9DCDTbFfqgXHFAlIaUUpRoFUufaBZHQPF6evHq/ud1fZQoaAZoCWgPQwhDjxg9d8FxQJSGlFKUaBVLkmgWR0Dxen/VSGahdX2UKGgGaAloD0MIYf91btq+cUCUhpRSlGgVS7FoFkdA8XqASK77K3V9lChoBmgJaA9DCAu45/nTDHJAlIaUUpRoFUuPaBZHQPF6gUMAmzB1fZQoaAZoCWgPQwiVKlH2VtFxQJSGlFKUaBVLomgWR0DxeoHEBbOedX2UKGgGaAloD0MI2A3bFqUAcUCUhpRSlGgVS6doFkdA8XqC3LFGX3V9lChoBmgJaA9DCJDAH35+YHFAlIaUUpRoFUumaBZHQPF6g8/GEPF1fZQoaAZoCWgPQwgp7Q2+cFN0QJSGlFKUaBVLrmgWR0DxeoQ3UhFFdX2UKGgGaAloD0MIH0sfuqBvcUCUhpRSlGgVS5poFkdA8XqFVlK9PHV9lChoBmgJaA9DCErQX+iRAnJAlIaUUpRoFUuoaBZHQPF6hibmU4d1ZS4="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 24416, "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-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

ppo-mlp-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:379f004fb3cc9dcf6e2540032f3e461a4c4181caa3253137e3f0ba2282e05ca4
+size 146564

ppo-mlp-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

ppo-mlp-LunarLander-v2/data

@@ -0,0 +1,92 @@
+{
+    "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 0x7f124846b820>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f124846b8b0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f124846b940>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f124846b9d0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f124846ba60>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f124846baf0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f124846bb80>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f124846bc10>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f124846bca0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f124846bd30>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f124846bdc0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f124846be50>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f124846c680>"
+    },
+    "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": 1,
+    "num_timesteps": 100007936,
+    "_total_timesteps": 100000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1679616981410490396,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": null,
+    "_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": -7.935999999997279e-05,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVHRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMInplgOFdYckCUhpRSlIwBbJRLkowBdJRHQPF51AfJV811fZQoaAZoCWgPQwjezOhHg7JzQJSGlFKUaBVLuWgWR0DxedRXU6PsdX2UKGgGaAloD0MISx5Pyw+YcECUhpRSlGgVS5toFkdA8Xoehx1gY3V9lChoBmgJaA9DCENwXMZNrXFAlIaUUpRoFUukaBZHQPF6H29i+cp1fZQoaAZoCWgPQwgcBvNXiLhyQJSGlFKUaBVLtmgWR0DxeiA+X7cgdX2UKGgGaAloD0MI+Wncm1+6cUCUhpRSlGgVS6VoFkdA8XogXUMG5nV9lChoBmgJaA9DCJRnXg57xXFAlIaUUpRoFUuUaBZHQPF6I9LRKHx1fZQoaAZoCWgPQwg57Sk5J91yQJSGlFKUaBVLsGgWR0DxeiRtPHktdX2UKGgGaAloD0MIuycPCzXwc0CUhpRSlGgVS8FoFkdA8XokY3WFvnV9lChoBmgJaA9DCAQ3UrYIM3FAlIaUUpRoFUuVaBZHQPF6Jb0voNd1fZQoaAZoCWgPQwhcyCO4kUdzQJSGlFKUaBVLxmgWR0DxeiYWTX8PdX2UKGgGaAloD0MINPPkmgLOckCUhpRSlGgVS7xoFkdA8XomgdKdx3V9lChoBmgJaA9DCGQFvw1xknFAlIaUUpRoFUuZaBZHQPF6J0Jmdy11fZQoaAZoCWgPQwg5tp4hHBNyQJSGlFKUaBVLqWgWR0DxeioePq9odX2UKGgGaAloD0MIBKkUO1quckCUhpRSlGgVS5poFkdA8Xoq9JnQIHV9lChoBmgJaA9DCFMkXwnkZXBAlIaUUpRoFUuXaBZHQPF6KvQD3dt1fZQoaAZoCWgPQwgVxausralzQJSGlFKUaBVLs2gWR0Dxeiy2vStvdX2UKGgGaAloD0MIkNeDSTEbc0CUhpRSlGgVS7doFkdA8Xos5r+HanV9lChoBmgJaA9DCNy5MNILGXBAlIaUUpRoFUubaBZHQPF6Lull9Sd1fZQoaAZoCWgPQwgnF2Ng3axwQJSGlFKUaBVLoWgWR0DxejBbX6IndX2UKGgGaAloD0MIMgIqHEEBckCUhpRSlGgVS55oFkdA8Xow53xFzHV9lChoBmgJaA9DCMeA7PUusXFAlIaUUpRoFUuhaBZHQPF6MR9/jKh1fZQoaAZoCWgPQwiIDRZO0hJwQJSGlFKUaBVLlGgWR0DxejUCUHIIdX2UKGgGaAloD0MIaObJNYWBckCUhpRSlGgVS6toFkdA8Xo2Oqebu3V9lChoBmgJaA9DCB2Txf0HxHJAlIaUUpRoFUuwaBZHQPF6NuYc/+t1fZQoaAZoCWgPQwjGi4UhMv1zQJSGlFKUaBVLumgWR0Dxejdgy/KydX2UKGgGaAloD0MIcyoZACptb0CUhpRSlGgVS6NoFkdA8Xo3iGi5/nV9lChoBmgJaA9DCMi3dw26xnBAlIaUUpRoFUukaBZHQPF6OFXPqs51fZQoaAZoCWgPQwiE8j6OJtpxQJSGlFKUaBVLsWgWR0DxejiU0vXcdX2UKGgGaAloD0MIIlUUr7JzcECUhpRSlGgVS5loFkdA8Xo60dvKl3V9lChoBmgJaA9DCGMraFqiwXFAlIaUUpRoFUupaBZHQPF6O6zLOiZ1fZQoaAZoCWgPQwj1DrdDAx9xQJSGlFKUaBVLm2gWR0Dxej0BDG96dX2UKGgGaAloD0MIeuQPBp62ckCUhpRSlGgVS7NoFkdA8Xo9gSOBD3V9lChoBmgJaA9DCERq2sV0W3FAlIaUUpRoFUuqaBZHQPF6PmACnxd1fZQoaAZoCWgPQwiVLCeh9KpxQJSGlFKUaBVLhmgWR0Dxej8qhDgJdX2UKGgGaAloD0MIpdqn47EicUCUhpRSlGgVS5NoFkdA8Xo/wwj+rHV9lChoBmgJaA9DCEuS5/o+MnNAlIaUUpRoFUuaaBZHQPF6QPPppvh1fZQoaAZoCWgPQwgQ5+EEZvhyQJSGlFKUaBVLuWgWR0DxekIg2qDLdX2UKGgGaAloD0MItoZSe5FVckCUhpRSlGgVS4doFkdA8XpEM63iJnV9lChoBmgJaA9DCG03wTcNpXBAlIaUUpRoFUuYaBZHQPF6RPDm8ul1fZQoaAZoCWgPQwjvN9pxQ5JzQJSGlFKUaBVLmmgWR0Dxekii5NGmdX2UKGgGaAloD0MIJVgcznxQckCUhpRSlGgVS6loFkdA8XpJbeVLSXV9lChoBmgJaA9DCGyYofHEyHNAlIaUUpRoFUuwaBZHQPF6Sg6PsAx1fZQoaAZoCWgPQwi6TbhXZp9zQJSGlFKUaBVLvmgWR0Dxeksk43m3dX2UKGgGaAloD0MIKIBiZAmQckCUhpRSlGgVS7VoFkdA8XpL6d1+zHV9lChoBmgJaA9DCPXb14GzPXFAlIaUUpRoFUuiaBZHQPF6TUhX8wZ1fZQoaAZoCWgPQwiNQ/0ubG9wQJSGlFKUaBVLtGgWR0Dxek5O1v2odX2UKGgGaAloD0MIa39ne7QacECUhpRSlGgVS6VoFkdA8XpPe6iCa3V9lChoBmgJaA9DCA6CjlY1UnNAlIaUUpRoFUuzaBZHQPF6UHUgB911fZQoaAZoCWgPQwhJgnAF1DZyQJSGlFKUaBVLlmgWR0DxelGFcpsodX2UKGgGaAloD0MI+S6lLlnucUCUhpRSlGgVS4toFkdA8XpRmZNO/XV9lChoBmgJaA9DCHnMQGX80HNAlIaUUpRoFUuvaBZHQPF6UjzOHFh1fZQoaAZoCWgPQwjmdi/3SVF0QJSGlFKUaBVLwmgWR0DxelNUp/gBdX2UKGgGaAloD0MIhugQOJIPc0CUhpRSlGgVS7poFkdA8XpT5fICEHV9lChoBmgJaA9DCJGYoIZvUHBAlIaUUpRoFUuaaBZHQPF6VXkbPyF1fZQoaAZoCWgPQwjsaYe/5opzQJSGlFKUaBVLrGgWR0Dxelam51/2dX2UKGgGaAloD0MIQfFjzJ15ckCUhpRSlGgVS4poFkdA8XpYWknCwnV9lChoBmgJaA9DCJAy4gJQTW9AlIaUUpRoFUuYaBZHQPF6WTuJDVp1fZQoaAZoCWgPQwiVnuklBntzQJSGlFKUaBVLo2gWR0DxelmptaZAdX2UKGgGaAloD0MIdm7ajFNrckCUhpRSlGgVS6loFkdA8XpddwBHTnV9lChoBmgJaA9DCGd+NQeI6nNAlIaUUpRoFUu3aBZHQPF6Xkj2SMd1fZQoaAZoCWgPQwi45LhTevNyQJSGlFKUaBVLtWgWR0DxemAjSofkdX2UKGgGaAloD0MIryMO2QBLckCUhpRSlGgVS7RoFkdA8XphEKNQ03V9lChoBmgJaA9DCGu7Cb4pJ3RAlIaUUpRoFUuzaBZHQPF6YiU8mrt1fZQoaAZoCWgPQwiD9urjoWxvQJSGlFKUaBVLlmgWR0DxemMDrJKbdX2UKGgGaAloD0MI/U0oREAxckCUhpRSlGgVS6xoFkdA8XpjaoMrmXV9lChoBmgJaA9DCGE1lrB2yHFAlIaUUpRoFUusaBZHQPF6Y4IiTt91fZQoaAZoCWgPQwgkJqjhWzxzQJSGlFKUaBVLuGgWR0DxemOiXpnpdX2UKGgGaAloD0MIKnReY9dAckCUhpRSlGgVS65oFkdA8XpkTYh+v3V9lChoBmgJaA9DCOS+1TqxvXJAlIaUUpRoFUuuaBZHQPF6Zd6MR6F1fZQoaAZoCWgPQwi7YHDN3TJyQJSGlFKUaBVLgWgWR0DxemX29crzdX2UKGgGaAloD0MIaRmp9xTAckCUhpRSlGgVS7JoFkdA8Xpn8CYCyXV9lChoBmgJaA9DCJc8npafWXBAlIaUUpRoFUuwaBZHQPF6aPQfIS11fZQoaAZoCWgPQwg9u3zrgytyQJSGlFKUaBVLnmgWR0DxemnAkLQYdX2UKGgGaAloD0MIBYnt7kGsc0CUhpRSlGgVS6BoFkdA8XpqYkAxSHV9lChoBmgJaA9DCBAf2PFfGm9AlIaUUpRoFUuIaBZHQPF6bhLBbfR1fZQoaAZoCWgPQwhb0HtjSKRzQJSGlFKUaBVLu2gWR0DxenEXOGCadX2UKGgGaAloD0MIXHLcKd1Qc0CUhpRSlGgVS5JoFkdA8XpxTG96C3V9lChoBmgJaA9DCJlH/mAghnNAlIaUUpRoFUu7aBZHQPF6ceU9pyp1fZQoaAZoCWgPQwjwT6kS5XpxQJSGlFKUaBVLp2gWR0DxenJ8nNPhdX2UKGgGaAloD0MIw0Xu6SoGckCUhpRSlGgVS5hoFkdA8Xpy2hM8HXV9lChoBmgJaA9DCBu4A3VKLXNAlIaUUpRoFUuVaBZHQPF6cvMbFS91fZQoaAZoCWgPQwgX1/hM9idyQJSGlFKUaBVLqGgWR0DxenT9Q40edX2UKGgGaAloD0MIHt5zYDmpc0CUhpRSlGgVS69oFkdA8Xp1kv4/NnV9lChoBmgJaA9DCL8OnDOiV3NAlIaUUpRoFUu2aBZHQPF6dw9r4351fZQoaAZoCWgPQwiLNPEO8J1zQJSGlFKUaBVLvGgWR0Dxenl1tO2zdX2UKGgGaAloD0MIH4MVpxo6cUCUhpRSlGgVS59oFkdA8Xp5j/6wdXV9lChoBmgJaA9DCGU1XU80ZXNAlIaUUpRoFUuuaBZHQPF6eh6F/QV1fZQoaAZoCWgPQwjA7QkSG2RyQJSGlFKUaBVLxGgWR0Dxeno+rELqdX2UKGgGaAloD0MIDkxuFBkJckCUhpRSlGgVS6JoFkdA8Xp6qMzdlHV9lChoBmgJaA9DCDTbFfqgXHFAlIaUUpRoFUufaBZHQPF6evHq/ud1fZQoaAZoCWgPQwhDjxg9d8FxQJSGlFKUaBVLkmgWR0Dxen/VSGahdX2UKGgGaAloD0MIYf91btq+cUCUhpRSlGgVS7FoFkdA8XqASK77K3V9lChoBmgJaA9DCAu45/nTDHJAlIaUUpRoFUuPaBZHQPF6gUMAmzB1fZQoaAZoCWgPQwiVKlH2VtFxQJSGlFKUaBVLomgWR0DxeoHEBbOedX2UKGgGaAloD0MI2A3bFqUAcUCUhpRSlGgVS6doFkdA8XqC3LFGX3V9lChoBmgJaA9DCJDAH35+YHFAlIaUUpRoFUumaBZHQPF6g8/GEPF1fZQoaAZoCWgPQwgp7Q2+cFN0QJSGlFKUaBVLrmgWR0DxeoQ3UhFFdX2UKGgGaAloD0MIH0sfuqBvcUCUhpRSlGgVS5poFkdA8XqFVlK9PHV9lChoBmgJaA9DCErQX+iRAnJAlIaUUpRoFUuoaBZHQPF6hibmU4d1ZS4="
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 24416,
+    "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-mlp-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dcf4430c20c926f053a022a52515f66d0f575d210de28e2656fb11db5201c286
+size 88057

ppo-mlp-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f2c2d7963d35265096031967e70e3d099c70e14a5131e526b20158bb774fb42c
+size 43393

ppo-mlp-LunarLander-v2/pytorch_variables.pth

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

ppo-mlp-LunarLander-v2/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.9.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 302.9949164746263, "std_reward": 20.230862536543793, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-25T03:18:11.576964"}