Upload PPO LunarLander-v2 trained agent

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,28 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 269.13 +/- 24.78
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+---
+
+  # **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
+  

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 0x7fce78ef0830>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fce78ef08c0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fce78ef0950>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fce78ef09e0>", "_build": "<function ActorCriticPolicy._build at 0x7fce78ef0a70>", "forward": "<function ActorCriticPolicy.forward at 0x7fce78ef0b00>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fce78ef0b90>", "_predict": "<function ActorCriticPolicy._predict at 0x7fce78ef0c20>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fce78ef0cb0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fce78ef0d40>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fce78ef0dd0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fce78f3e720>"}, "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": 1652183266.3319435, "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:": "gAWVbhAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMI9IdmnlxhbECUhpRSlIwBbJRNUwGMAXSUR0CxOlPQv6CUdX2UKGgGaAloD0MI1q2ek15DbUCUhpRSlGgVTTEBaBZHQLE6WIDHOr11fZQoaAZoCWgPQwhpccYwp0twQJSGlFKUaBVNGwFoFkdAsTqTapPykXV9lChoBmgJaA9DCDgyj/yBTHJAlIaUUpRoFU1lAWgWR0CxOwJTdcjadX2UKGgGaAloD0MIRfEqa1v5cECUhpRSlGgVTQIBaBZHQLE7FsxO+Ix1fZQoaAZoCWgPQwjluFM62H1xQJSGlFKUaBVNbwFoFkdAsTurxCpm3HV9lChoBmgJaA9DCOtSI/Szt21AlIaUUpRoFU0DAWgWR0CxO7oouwotdX2UKGgGaAloD0MIIuLmVPKEcUCUhpRSlGgVTSgBaBZHQLE7zm9xp+N1fZQoaAZoCWgPQwhEF9S3TEZyQJSGlFKUaBVNNAFoFkdAsTvTeSB9TnV9lChoBmgJaA9DCAqd19glCWxAlIaUUpRoFU0pAWgWR0CxO+EhV2iddX2UKGgGaAloD0MIvxBy3n+zbkCUhpRSlGgVS/5oFkdAsTvuy0KJEnV9lChoBmgJaA9DCG1y+KSTWm9AlIaUUpRoFUv6aBZHQLE8JMotthx1fZQoaAZoCWgPQwitFAK5hARwQJSGlFKUaBVNJAFoFkdAsTxi19fCynV9lChoBmgJaA9DCHhEhermklVAlIaUUpRoFUuZaBZHQLE8eR51Ng11fZQoaAZoCWgPQwjSVE/mHwVxQJSGlFKUaBVNMQFoFkdAsTyvYwqRU3V9lChoBmgJaA9DCDYhrTEoxHBAlIaUUpRoFU0IAWgWR0CxPLYpDu0DdX2UKGgGaAloD0MItFn1uVr9b0CUhpRSlGgVTQoBaBZHQLE8tmeDnNh1fZQoaAZoCWgPQwihEtcx7uBzQJSGlFKUaBVNDwFoFkdAsTzBvES/TXV9lChoBmgJaA9DCK9eRUaH+XFAlIaUUpRoFU0zAWgWR0CxPL+RPoFFdX2UKGgGaAloD0MITU7tDFOLRUCUhpRSlGgVS85oFkdAsTzXNfPX1HV9lChoBmgJaA9DCDNv1XWoUW5AlIaUUpRoFU0/AWgWR0CxPVLuYx+KdX2UKGgGaAloD0MIKsdkcX/zcECUhpRSlGgVTSEBaBZHQLE+NjNY8uB1fZQoaAZoCWgPQwiE2JlCZ0RvQJSGlFKUaBVNGgFoFkdAsT5gFSsKcHV9lChoBmgJaA9DCIts5/vpoHBAlIaUUpRoFU0vAWgWR0CxPm+HFglXdX2UKGgGaAloD0MI9IqnHmk2RECUhpRSlGgVS8toFkdAsT6T/dZaFHV9lChoBmgJaA9DCPuUY7J4eXFAlIaUUpRoFU05AWgWR0CxPqgt4A0bdX2UKGgGaAloD0MI4ExMF+IdcECUhpRSlGgVTSwBaBZHQLE+pz3h4t91fZQoaAZoCWgPQwgDXfsC+gJxQJSGlFKUaBVNFQFoFkdAsT74kleF+XV9lChoBmgJaA9DCJlmutfJVmxAlIaUUpRoFU1EAWgWR0CxPyyNn5BUdX2UKGgGaAloD0MI/p5Yp8qhb0CUhpRSlGgVTSIBaBZHQLE/N7ZFoct1fZQoaAZoCWgPQwind/F+XKdwQJSGlFKUaBVNEwFoFkdAsT9TvjOs1nV9lChoBmgJaA9DCLhYUYNpu3JAlIaUUpRoFU0lAWgWR0CxP4wssg+ydX2UKGgGaAloD0MI0AziA7sxcECUhpRSlGgVTTUBaBZHQLE/snsLORl1fZQoaAZoCWgPQwjt1jIZDhZzQJSGlFKUaBVNpgFoFkdAsT+7x+az/3V9lChoBmgJaA9DCDAt6pPcaW9AlIaUUpRoFU0yAWgWR0CxP8nhCMP0dX2UKGgGaAloD0MIEeLK2Xs8ckCUhpRSlGgVTU8BaBZHQLE/2IHTqjd1fZQoaAZoCWgPQwiTADW1rBVyQJSGlFKUaBVNGAFoFkdAsUASYSg5BHV9lChoBmgJaA9DCA3DR8QUPXJAlIaUUpRoFU0BAWgWR0CxQJ79If8udX2UKGgGaAloD0MIEYsYdpgyc0CUhpRSlGgVS/9oFkdAsUDAiB5HE3V9lChoBmgJaA9DCLKEtTF2225AlIaUUpRoFU0KAWgWR0CxQRkWuX/pdX2UKGgGaAloD0MIbm3heSkocUCUhpRSlGgVS/JoFkdAsUgznwG4Z3V9lChoBmgJaA9DCDM1Cd6QS3BAlIaUUpRoFU0uAWgWR0CxSFFSn+AFdX2UKGgGaAloD0MII/YJoJhrcECUhpRSlGgVS/1oFkdAsUhdIH1OCXV9lChoBmgJaA9DCHegTnm0eHFAlIaUUpRoFUvlaBZHQLFIeWXkYGd1fZQoaAZoCWgPQwgZNzXQvANwQJSGlFKUaBVNWQFoFkdAsUiMh5gPVnV9lChoBmgJaA9DCOMW83PDpG9AlIaUUpRoFU0nAWgWR0CxSI5gLJCCdX2UKGgGaAloD0MI3PC76RZocECUhpRSlGgVTQ0BaBZHQLFIn6HTI/91fZQoaAZoCWgPQwhKuJBHcJNwQJSGlFKUaBVL7GgWR0CxSKvEKmbcdX2UKGgGaAloD0MI8ppXdRZIcECUhpRSlGgVTQwBaBZHQLFJF2UjcEh1fZQoaAZoCWgPQwg6JLVQMsxuQJSGlFKUaBVNKwFoFkdAsUk76i0v5HV9lChoBmgJaA9DCPbtJCL8OW5AlIaUUpRoFU0xAWgWR0CxSVY4ZMtcdX2UKGgGaAloD0MIyF7v/ngxTkCUhpRSlGgVS9doFkdAsUlxmK64D3V9lChoBmgJaA9DCKGDLuGQQXNAlIaUUpRoFU3AAWgWR0CxSYKt9x6wdX2UKGgGaAloD0MIO3E5XkFecUCUhpRSlGgVTSUBaBZHQLFJjI7/4qR1fZQoaAZoCWgPQwibOo+Kf+JvQJSGlFKUaBVL+WgWR0CxSc6U3XI2dX2UKGgGaAloD0MIKEUr9wIWckCUhpRSlGgVTQYBaBZHQLFKQThHbyp1fZQoaAZoCWgPQwjX+Ez2zyFyQJSGlFKUaBVL/GgWR0CxSmXarWAgdX2UKGgGaAloD0MIBd80fTahcUCUhpRSlGgVTQYBaBZHQLFKozvZyuJ1fZQoaAZoCWgPQwgwnkFDfyZxQJSGlFKUaBVNFwFoFkdAsUr+reZXuHV9lChoBmgJaA9DCIqsNZRazHFAlIaUUpRoFU0VAWgWR0CxSx3c1wYMdX2UKGgGaAloD0MIM/0S8VYzckCUhpRSlGgVTR4BaBZHQLFLJcynDSB1fZQoaAZoCWgPQwix+E1hpeRDQJSGlFKUaBVL6GgWR0CxS1VLOAy3dX2UKGgGaAloD0MIQSybOaQlcUCUhpRSlGgVTQIBaBZHQLFLcKT0QK91fZQoaAZoCWgPQwiSkh6GFgJwQJSGlFKUaBVNZwFoFkdAsUuQKsuFpXV9lChoBmgJaA9DCMA+OnWloXJAlIaUUpRoFU0BAWgWR0CxS7IhllK9dX2UKGgGaAloD0MIfepYpTRNckCUhpRSlGgVTYMBaBZHQLFL8ZkkKNR1fZQoaAZoCWgPQwg900uM5QhvQJSGlFKUaBVNHQFoFkdAsUwRtrKvFHV9lChoBmgJaA9DCPBRf71CoXFAlIaUUpRoFU0oAWgWR0CxTD8yrPt2dX2UKGgGaAloD0MIVKcDWY9ickCUhpRSlGgVTaEBaBZHQLFMTS5y2hJ1fZQoaAZoCWgPQwjBkNWtHlBsQJSGlFKUaBVNFQFoFkdAsUxqEpRXOnV9lChoBmgJaA9DCK34hsLn8W5AlIaUUpRoFU1OAWgWR0CxTJm4iHIqdX2UKGgGaAloD0MI3zXoS69acUCUhpRSlGgVTRgBaBZHQLFM1TOxB3R1fZQoaAZoCWgPQwgcCp+tA7tzQJSGlFKUaBVNAQFoFkdAsUz6fEn9enV9lChoBmgJaA9DCLcLzXXa7XFAlIaUUpRoFU0rAWgWR0CxTSCPQv6CdX2UKGgGaAloD0MIAJATJswMc0CUhpRSlGgVS/ZoFkdAsU0yRhc7hnV9lChoBmgJaA9DCObpXFFKIklAlIaUUpRoFUuwaBZHQLFNlHnU2DR1fZQoaAZoCWgPQwhUbqKWJv5xQJSGlFKUaBVNKQFoFkdAsU3ODEm6XnV9lChoBmgJaA9DCPn02JbB8HFAlIaUUpRoFU06AWgWR0CxTfEornTzdX2UKGgGaAloD0MIeXk6VxTvb0CUhpRSlGgVTSkBaBZHQLFN/CNCJGh1fZQoaAZoCWgPQwgSSl8I+YFyQJSGlFKUaBVNOwFoFkdAsU5BAgPmP3V9lChoBmgJaA9DCBnmBG2yKnJAlIaUUpRoFU1AAWgWR0CxTmxLoOhCdX2UKGgGaAloD0MIOSf20P5QcUCUhpRSlGgVTSUBaBZHQLFOk3kgfU51fZQoaAZoCWgPQwind/F+HDJwQJSGlFKUaBVL92gWR0CxTqDnied1dX2UKGgGaAloD0MIRmEXRY9HbUCUhpRSlGgVTQ8BaBZHQLFOt4RVZLZ1fZQoaAZoCWgPQwjwTdNnR6ZwQJSGlFKUaBVNZAFoFkdAsU7gDDCP63V9lChoBmgJaA9DCO26tyJxF3JAlIaUUpRoFU0BAWgWR0CxTuj6eoUBdX2UKGgGaAloD0MIP1OvW4Smb0CUhpRSlGgVS/toFkdAsU896Uqx1XV9lChoBmgJaA9DCPjhICFKNHFAlIaUUpRoFU1YAWgWR0CxT0eoYNy6dX2UKGgGaAloD0MI1elA1tOMb0CUhpRSlGgVTSIBaBZHQLFPa5/9YOl1fZQoaAZoCWgPQwgUkszqHe9xQJSGlFKUaBVNIAFoFkdAsU/BOfukUXV9lChoBmgJaA9DCONUa2FWmnBAlIaUUpRoFUvsaBZHQLFP45bQkX11fZQoaAZoCWgPQwiASpUou5RwQJSGlFKUaBVNPgFoFkdAsU/zM0P6K3V9lChoBmgJaA9DCMGPatjvy3JAlIaUUpRoFU0oAWgWR0CxUDnOnl4kdX2UKGgGaAloD0MIhc0AF2QIb0CUhpRSlGgVTQcBaBZHQLFQQ3lCCz11fZQoaAZoCWgPQwgVPIVcKSVxQJSGlFKUaBVNEgFoFkdAsVBmXu3MIXV9lChoBmgJaA9DCBYTm49rgWxAlIaUUpRoFU03AWgWR0CxUQmBas6rdX2UKGgGaAloD0MIba0vEhprcUCUhpRSlGgVTQUBaBZHQLFRDEhq0t11fZQoaAZoCWgPQwiHpYEfFTlyQJSGlFKUaBVNLQFoFkdAsVEfO5avBHV9lChoBmgJaA9DCMtHUtLDonBAlIaUUpRoFU0jAWgWR0CxUS+7QLNOdX2UKGgGaAloD0MIXeFdLuK8cECUhpRSlGgVTR8BaBZHQLFRM863iJh1ZS4="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 372, "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-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022", "Python": "3.7.13", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0+cu113", "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:fd20ef8cadb511f41b56cbcfd438a68e8f6e5db08e6574e6f9ae52e0e281f168
+size 144090

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.5.0

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 0x7fce78ef0830>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fce78ef08c0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fce78ef0950>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fce78ef09e0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fce78ef0a70>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fce78ef0b00>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fce78ef0b90>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fce78ef0c20>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fce78ef0cb0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fce78ef0d40>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fce78ef0dd0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fce78f3e720>"
+    },
+    "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": 1652183266.3319435,
+    "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": 372,
+    "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 sha256:21165c91670545e79fe925765fadf731dc20e9935e9d6173caec31ecd411ca55
+size 84893

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2fc6d665d4cd2c07b841c4b818b65d7e00e8ed0fdfa53b04380590cb1864063e
+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-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
+Python: 3.7.13
+Stable-Baselines3: 1.5.0
+PyTorch: 1.11.0+cu113
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dbd09101c69ff8b10f278648a4a0a52b97d2242a6c8629d92122a8859525290e
+size 193474

results.json

@@ -0,0 +1 @@
+{"mean_reward": 269.12991543304173, "std_reward": 24.775860648373232, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-10T13:03:22.381474"}