Add first model

.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: -37.10 +/- 72.81
+      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 0x7f3055ffa950>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3055ffa9e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3055ffaa70>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3055ffab00>", "_build": "<function ActorCriticPolicy._build at 0x7f3055ffab90>", "forward": "<function ActorCriticPolicy.forward at 0x7f3055ffac20>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f3055ffacb0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f3055ffad40>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f3055ffadd0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f3055ffae60>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f3055ffaef0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f30560b3f30>"}, "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": 524288, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1651961843.6960242, "learning_rate": 0.0001, "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": -0.04857599999999995, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVfxAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIDD7NyYt4TkCUhpRSlIwBbJRN6AOMAXSUR0CPPBnied08dX2UKGgGaAloD0MID9JT5BDBFMCUhpRSlGgVTQ8BaBZHQI9M/a+N96V1fZQoaAZoCWgPQwjE6SRbXexXQJSGlFKUaBVN6ANoFkdAj1hYe9zwMHV9lChoBmgJaA9DCLKFIAclDl9AlIaUUpRoFU3oA2gWR0CPX+GTs6aLdX2UKGgGaAloD0MIbAa4IFvmX0CUhpRSlGgVTegDaBZHQI9kH7vXsgN1fZQoaAZoCWgPQwhmh/iHLUtTQJSGlFKUaBVN6ANoFkdAj2clnRLK3nV9lChoBmgJaA9DCIohOZm49F9AlIaUUpRoFU3oA2gWR0CPaPA44p+ddX2UKGgGaAloD0MIkrHa/L8jV0CUhpRSlGgVTegDaBZHQI9zBuXNTtN1fZQoaAZoCWgPQwi5jnHFxeVKwJSGlFKUaBVNSwNoFkdAj3oNG/etS3V9lChoBmgJaA9DCG6kbJG0s19AlIaUUpRoFU3oA2gWR0CPfrk/bCaadX2UKGgGaAloD0MI93ghHR6qYUCUhpRSlGgVTegDaBZHQI+MmHgxagV1fZQoaAZoCWgPQwjFAl/RrVM1wJSGlFKUaBVNPwFoFkdAj5QXQ2MsH3V9lChoBmgJaA9DCMGtu3mqMydAlIaUUpRoFUvsaBZHQI+WgJXyRSx1fZQoaAZoCWgPQwjAPjp15VxbQJSGlFKUaBVN6ANoFkdAj5r/e+Eh7nV9lChoBmgJaA9DCLdgqS7gQlJAlIaUUpRoFU3oA2gWR0CPws8SwnpjdX2UKGgGaAloD0MIsi/ZeLApV0CUhpRSlGgVTegDaBZHQI/EGOyVv/B1fZQoaAZoCWgPQwhOfotOltpYQJSGlFKUaBVN6ANoFkdAj8T/pUxVQ3V9lChoBmgJaA9DCLplh/iHTRXAlIaUUpRoFU0vAWgWR0CPxkhouf29dX2UKGgGaAloD0MIhUTaxp/7XECUhpRSlGgVTegDaBZHQI/NmbqhUR51fZQoaAZoCWgPQwiwVu2akORRQJSGlFKUaBVN6ANoFkdAj9vLh73PA3V9lChoBmgJaA9DCKUxWkdV619AlIaUUpRoFU3oA2gWR0CP7P/xUedTdX2UKGgGaAloD0MIHAqfrYOkWUCUhpRSlGgVTegDaBZHQI/4IE6kqMF1fZQoaAZoCWgPQwg7Vb5nJCJSQJSGlFKUaBVN6ANoFkdAj/9p4SpR43V9lChoBmgJaA9DCP/QzJNr1FhAlIaUUpRoFU3oA2gWR0CQA1+GGmDUdX2UKGgGaAloD0MIZrtCHyyPXECUhpRSlGgVTegDaBZHQJAET2IwdsB1fZQoaAZoCWgPQwjYD7HBQkVvwJSGlFKUaBVN0wFoFkdAkAwyyQgcLnV9lChoBmgJaA9DCGOcvwmFnF9AlIaUUpRoFU3oA2gWR0CQDRNZNfw7dX2UKGgGaAloD0MI0LUvoBdDW0CUhpRSlGgVTegDaBZHQJAPc4jrzGx1fZQoaAZoCWgPQwiP/wJBgNtbQJSGlFKUaBVN6ANoFkdAkJ3ZbQkX13V9lChoBmgJaA9DCA+0AkNWKl5AlIaUUpRoFU3oA2gWR0CQosQ8wHqvdX2UKGgGaAloD0MIxanWwiyuWECUhpRSlGgVTegDaBZHQJClB11W8yx1fZQoaAZoCWgPQwj6K2SuDPNUQJSGlFKUaBVN6ANoFkdAkLkhFuvU0HV9lChoBmgJaA9DCJ4j8l1KkV5AlIaUUpRoFU3oA2gWR0CQub/Ot4iYdX2UKGgGaAloD0MIuAGfH0Y4WkCUhpRSlGgVTegDaBZHQJC6M91U2k11fZQoaAZoCWgPQwjqBgq8k6ZgQJSGlFKUaBVN6ANoFkdAkLrUXHim23V9lChoBmgJaA9DCN44Kcx7lCfAlIaUUpRoFU0JAWgWR0CQu3Bv73wkdX2UKGgGaAloD0MImRJJ9DILXUCUhpRSlGgVTegDaBZHQJDE0gvDgqF1fZQoaAZoCWgPQwhd4PJYs+9mQJSGlFKUaBVNtgFoFkdAkMYNcKPXCnV9lChoBmgJaA9DCNkJL8Gps15AlIaUUpRoFU3oA2gWR0CQzNwoLG70dX2UKGgGaAloD0MIjIS2nEsJV0CUhpRSlGgVTegDaBZHQJDSAOOKfnR1fZQoaAZoCWgPQwgmcyzvqvFTQJSGlFKUaBVN6ANoFkdAkNVizHCGe3V9lChoBmgJaA9DCLr3cMlxwV9AlIaUUpRoFU3oA2gWR0CQ2MuLrHENdX2UKGgGaAloD0MIM+IC0CgnV0CUhpRSlGgVTegDaBZHQJDZtowmE5B1fZQoaAZoCWgPQwjy7V2Dvr9XQJSGlFKUaBVN6ANoFkdAkOIFO45LiHV9lChoBmgJaA9DCM0jfzBwH2FAlIaUUpRoFU3oA2gWR0CQ4unVoYeldX2UKGgGaAloD0MIyJkmbD+YUUCUhpRSlGgVTegDaBZHQJDleIBRyfd1fZQoaAZoCWgPQwh2pPrOL+FSQJSGlFKUaBVN6ANoFkdAkO1xKg7HQ3V9lChoBmgJaA9DCJnTZTGxDTtAlIaUUpRoFU3oA2gWR0CRDz/UONHZdX2UKGgGaAloD0MIPBVwz/N4WECUhpRSlGgVTegDaBZHQJEP/xRVIZt1fZQoaAZoCWgPQwhDjNe8KgVhQJSGlFKUaBVN6ANoFkdAkRB4FmnO0XV9lChoBmgJaA9DCKQAUTBjlFNAlIaUUpRoFU3oA2gWR0CREThcJMQFdX2UKGgGaAloD0MI1lWBWgzFW0CUhpRSlGgVTegDaBZHQJER5PgvUSZ1fZQoaAZoCWgPQwjhtOBFXyEmQJSGlFKUaBVN6ANoFkdAkRxXTVlPJ3V9lChoBmgJaA9DCGtHcY46tlFAlIaUUpRoFU3oA2gWR0CRHcYfnwG4dX2UKGgGaAloD0MIjo8WZwxUXUCUhpRSlGgVTegDaBZHQJElN13dKul1fZQoaAZoCWgPQwhMGM3K9sE+QJSGlFKUaBVL/GgWR0CRJiTRIBikdX2UKGgGaAloD0MI/Bu0Vx/0WECUhpRSlGgVTegDaBZHQJEq3wrlNlB1fZQoaAZoCWgPQwgFGQEVjtxdQJSGlFKUaBVN6ANoFkdAkS5XV9Wp63V9lChoBmgJaA9DCN4+q8yU4ktAlIaUUpRoFU3oA2gWR0CRMdZ8a4tpdX2UKGgGaAloD0MIOIWVCioqRECUhpRSlGgVTegDaBZHQJEyvWsijcp1fZQoaAZoCWgPQwhSKAtfX1M/wJSGlFKUaBVNZgFoFkdAkTo83Q2MsHV9lChoBmgJaA9DCGhAvRk18z1AlIaUUpRoFU3oA2gWR0CROo9wFTvRdX2UKGgGaAloD0MIkIXoEDgaJ8CUhpRSlGgVTYMBaBZHQJE7HQXyiEh1fZQoaAZoCWgPQwiFzQAXZHM/QJSGlFKUaBVN6ANoFkdAkTtPvnbItHV9lChoBmgJaA9DCJV/La9c8lBAlIaUUpRoFU3oA2gWR0CRPWD5CWu6dX2UKGgGaAloD0MI9ifxuZNYYsCUhpRSlGgVTXsBaBZHQJHJuAJ9iMJ1fZQoaAZoCWgPQwi0WIrkK5RXQJSGlFKUaBVN6ANoFkdAkcrocNpdr3V9lChoBmgJaA9DCEKwql7+AGJAlIaUUpRoFU3CA2gWR0CR5fBNmDlHdX2UKGgGaAloD0MIMPZefNHFWUCUhpRSlGgVTegDaBZHQJHpIRywOe91fZQoaAZoCWgPQwjpmPOMfcNTwJSGlFKUaBVNywFoFkdAkel3/1g6VHV9lChoBmgJaA9DCKYpApze20ZAlIaUUpRoFU3oA2gWR0CR6kegte2NdX2UKGgGaAloD0MISvHxCVm/YUCUhpRSlGgVTegDaBZHQJHrvdLxqfx1fZQoaAZoCWgPQwjmdcQhG05cQJSGlFKUaBVN6ANoFkdAkgETodMj/3V9lChoBmgJaA9DCGeasP1kSEFAlIaUUpRoFU3oA2gWR0CSB7B6a9bpdX2UKGgGaAloD0MIPgYrTrVtVECUhpRSlGgVTegDaBZHQJIL4TlDF611fZQoaAZoCWgPQwhpcjEG1ppSQJSGlFKUaBVN6ANoFkdAkg/QUHpr13V9lChoBmgJaA9DCLJnz2VqGEpAlIaUUpRoFU3oA2gWR0CSENQEpy6udX2UKGgGaAloD0MIByXMtP33WkCUhpRSlGgVTegDaBZHQJIZHPAwfyR1fZQoaAZoCWgPQwjkEHFzKtBUQJSGlFKUaBVN6ANoFkdAkhlx2fTTfHV9lChoBmgJaA9DCEWA07t410BAlIaUUpRoFU3oA2gWR0CSGg5R0lqrdX2UKGgGaAloD0MItiv0wTIJU0CUhpRSlGgVTegDaBZHQJIcdcC5mRN1fZQoaAZoCWgPQwjdfCO6Z+BhQJSGlFKUaBVN6ANoFkdAkiGfiHZbp3V9lChoBmgJaA9DCC/3yVGApEdAlIaUUpRoFU3oA2gWR0CSIui97F85dX2UKGgGaAloD0MIAKyOHOnANcCUhpRSlGgVTZwBaBZHQJI56wzLwF11fZQoaAZoCWgPQwh/2T15WIJaQJSGlFKUaBVN6ANoFkdAkjyGQ4jrzHV9lChoBmgJaA9DCDMXuDzWbAZAlIaUUpRoFU1EAWgWR0CSPRBMi8nNdX2UKGgGaAloD0MIenHiqx3SXECUhpRSlGgVTegDaBZHQJI/MEkjX4F1fZQoaAZoCWgPQwheZW1TPIBaQJSGlFKUaBVN6ANoFkdAkj9uclPac3V9lChoBmgJaA9DCOdwrfawjVxAlIaUUpRoFU3oA2gWR0CSQBSwnpjddX2UKGgGaAloD0MIvHfUmBDnVUCUhpRSlGgVTegDaBZHQJJBK6d1+y91fZQoaAZoCWgPQwiESfHxCYEqwJSGlFKUaBVNnAFoFkdAkkR/QOWjXXV9lChoBmgJaA9DCHOCNjl8AhrAlIaUUpRoFU3oA2gWR0CSUZWSEDhcdX2UKGgGaAloD0MI6USCqWZPVkCUhpRSlGgVTegDaBZHQJJW9PN3W4F1fZQoaAZoCWgPQwjmd5rMeE5WQJSGlFKUaBVN6ANoFkdAklp9j0+TvHV9lChoBmgJaA9DCNB+pIgMlFhAlIaUUpRoFU3oA2gWR0CSXhmR/3FldX2UKGgGaAloD0MIR+UmaumMYkCUhpRSlGgVTegDaBZHQJJfEIIF/x51fZQoaAZoCWgPQwga+ie4WPk6QJSGlFKUaBVN6ANoFkdAkmeXVXmvGXV9lChoBmgJaA9DCAGG5c+3YV1AlIaUUpRoFU3oA2gWR0CSaEQ3gk1NdX2UKGgGaAloD0MIrTJTWn/3aUCUhpRSlGgVTewBaBZHQJJqs+zMRpV1fZQoaAZoCWgPQwgPfuIA+h1cQJSGlFKUaBVN6ANoFkdAkmsFBt1p03VlLg=="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 160, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "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:d8b4f66b43fcf00e5cbc5045ce83e9ed90aa227b8d83d514e58d09d896629f7a
+size 144110

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 0x7f3055ffa950>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3055ffa9e0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3055ffaa70>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3055ffab00>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f3055ffab90>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f3055ffac20>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f3055ffacb0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f3055ffad40>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f3055ffadd0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f3055ffae60>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f3055ffaef0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f30560b3f30>"
+    },
+    "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": 524288,
+    "_total_timesteps": 500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1651961843.6960242,
+    "learning_rate": 0.0001,
+    "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": -0.04857599999999995,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 160,
+    "n_steps": 2048,
+    "gamma": 0.99,
+    "gae_lambda": 0.95,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 10,
+    "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 sha256:53016e1b0165cddc4c247f1812d27e466f119daba7f6e12060deb0dabef165ae
+size 84893

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9adec793ca8113e62afc7cc19c4c75cf6f0bf03f56ba3dcf17a5b94d07888d0e
+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:ca3cf05133a33eef61947d1e0a349f616cc910263cd07afbe0d6fb4dc7fa226f
+size 248207

results.json

@@ -0,0 +1 @@
+{"mean_reward": -37.0980689476477, "std_reward": 72.80870201435035, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-07T22:49:45.541736"}