demo colab training agent ppo-LunarLander-v2

.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: 67.12 +/- 97.38
+      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 0x7fdd143e2d40>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fdd143e2dd0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fdd143e2e60>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fdd143e2ef0>", "_build": "<function ActorCriticPolicy._build at 0x7fdd143e2f80>", "forward": "<function ActorCriticPolicy.forward at 0x7fdd143eb050>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fdd143eb0e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fdd143eb170>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fdd143eb200>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fdd143eb290>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fdd143eb320>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fdd144420f0>"}, "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": 507904, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1651765686.410721, "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": 124, "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 sha256:921d55ef3386e3ebcba0637728898488da7c31ed86e5533bc2c81a7f37c59f81
+size 144043

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 0x7fdd143e2d40>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fdd143e2dd0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fdd143e2e60>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fdd143e2ef0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fdd143e2f80>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fdd143eb050>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fdd143eb0e0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fdd143eb170>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fdd143eb200>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fdd143eb290>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fdd143eb320>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fdd144420f0>"
+    },
+    "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": 507904,
+    "_total_timesteps": 500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1651765686.410721,
+    "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:": "gAWVfhAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMID/Ckhcu6VsCUhpRSlIwBbJRNqwGMAXSUR0CClIvkBCD3dX2UKGgGaAloD0MIZyjueJPzW0CUhpRSlGgVTegDaBZHQIKbyYG+sYF1fZQoaAZoCWgPQwg/cQD9voVWQJSGlFKUaBVN6ANoFkdAgqIFotcv/XV9lChoBmgJaA9DCJ5flKC/eFpAlIaUUpRoFU3oA2gWR0CCp2u27Wd3dX2UKGgGaAloD0MIxw4qcR3ZUUCUhpRSlGgVTegDaBZHQIKyyhUR3/x1fZQoaAZoCWgPQwiTj90FSjhMQJSGlFKUaBVN6ANoFkdAgrpVRUFSsXV9lChoBmgJaA9DCHV3nQ35tl9AlIaUUpRoFU3oA2gWR0CCxyb2lEZ0dX2UKGgGaAloD0MId9Zuu9D4W0CUhpRSlGgVTegDaBZHQILUiSX+l0p1fZQoaAZoCWgPQwjdJAaBlQJUQJSGlFKUaBVN6ANoFkdAgtugH3UQTXV9lChoBmgJaA9DCH3ogvqWoV9AlIaUUpRoFU3oA2gWR0CC3Tdszl90dX2UKGgGaAloD0MILubnhqY0UkCUhpRSlGgVTegDaBZHQILfEqMFUyZ1fZQoaAZoCWgPQwif508b1VheQJSGlFKUaBVN6ANoFkdAgyk6bONYKnV9lChoBmgJaA9DCKweMA+ZDVlAlIaUUpRoFU3oA2gWR0CDLMnxaxHHdX2UKGgGaAloD0MIZoUi3U+iY0CUhpRSlGgVTegDaBZHQIMzqgdwNsp1fZQoaAZoCWgPQwiTisba3zE2wJSGlFKUaBVNiwFoFkdAgzTk6T4cm3V9lChoBmgJaA9DCMLDtG/upyhAlIaUUpRoFU0KAWgWR0CDPdQBxPwedX2UKGgGaAloD0MIfo/66xWnWUCUhpRSlGgVTegDaBZHQINTIkZ75VR1fZQoaAZoCWgPQwhRMc7fhKVVQJSGlFKUaBVN6ANoFkdAg1igQYk3THV9lChoBmgJaA9DCL9+iA0WZ1pAlIaUUpRoFU3oA2gWR0CDdymqHXVcdX2UKGgGaAloD0MIVRhbCHKQYECUhpRSlGgVTegDaBZHQIN+IAfdRBN1fZQoaAZoCWgPQwjT2jS211BSQJSGlFKUaBVN6ANoFkdAg4QNmthd+3V9lChoBmgJaA9DCKnZA63AXl5AlIaUUpRoFU3oA2gWR0CDiQtQKrq/dX2UKGgGaAloD0MIcv4mFCJRXUCUhpRSlGgVTegDaBZHQIOT42fkFOh1fZQoaAZoCWgPQwiOQLyuX1pYQJSGlFKUaBVN6ANoFkdAg6ZfyoXKsHV9lChoBmgJaA9DCBBc5QmED1BAlIaUUpRoFU3oA2gWR0CDskfxMFlkdX2UKGgGaAloD0MIx4FXy53XYMCUhpRSlGgVTf8CaBZHQIO2ezjWCmN1fZQoaAZoCWgPQwg6sYf2sTNTQJSGlFKUaBVN6ANoFkdAg7kOCf6Gg3V9lChoBmgJaA9DCIRhwJKrilBAlIaUUpRoFU3oA2gWR0CDvF9ZzPrwdX2UKGgGaAloD0MI1sQCX9E5YECUhpRSlGgVTegDaBZHQIQG2o99tuV1fZQoaAZoCWgPQwjTS4xl+lNlQJSGlFKUaBVNJAJoFkdAhAlCnHeaa3V9lChoBmgJaA9DCFTjpZvECltAlIaUUpRoFU3oA2gWR0CECnu2qkuZdX2UKGgGaAloD0MI+GuyRj2iQsCUhpRSlGgVTegDaBZHQIQQzwKBuoB1fZQoaAZoCWgPQwi+UMB2MB5dQJSGlFKUaBVN6ANoFkdAhBn2SlnAZnV9lChoBmgJaA9DCKEuUiiLUGBAlIaUUpRoFU3oA2gWR0CELc5VfeDWdX2UKGgGaAloD0MIaK7TSEsYXkCUhpRSlGgVTegDaBZHQIQzE3Q2MsJ1fZQoaAZoCWgPQwi+9WG9UT9BwJSGlFKUaBVNTwFoFkdAhD4nndO6/nV9lChoBmgJaA9DCJKU9DC0Uk1AlIaUUpRoFU3oA2gWR0CEV9F2mpEQdX2UKGgGaAloD0MIbhgFweOCWkCUhpRSlGgVTegDaBZHQIRd4RPGhmJ1fZQoaAZoCWgPQwgDJ9vAHVlcQJSGlFKUaBVN6ANoFkdAhGMbpu/DcnV9lChoBmgJaA9DCDgu46YGb1tAlIaUUpRoFU3oA2gWR0CEbr0eU6gedX2UKGgGaAloD0MIF5tWCoHAYkCUhpRSlGgVTegDaBZHQISCVWsA/9p1fZQoaAZoCWgPQwjURnU6kLUiwJSGlFKUaBVL/GgWR0CEhDDjzZpSdX2UKGgGaAloD0MIppnudVLxV0CUhpRSlGgVTegDaBZHQISPRuAI6bR1fZQoaAZoCWgPQwit+lxtRUJhQJSGlFKUaBVN6ANoFkdAhJOOzQeFL3V9lChoBmgJaA9DCObKoNrgMVFAlIaUUpRoFU3oA2gWR0CElkZuyeI3dX2UKGgGaAloD0MIa7jIPV1wYkCUhpRSlGgVTegDaBZHQISZv6TGHYZ1fZQoaAZoCWgPQwhnSBXFqxJKQJSGlFKUaBVN6ANoFkdAhOR2q94/vHV9lChoBmgJaA9DCCQO2UC6VV9AlIaUUpRoFU3oA2gWR0CE5vN5dGAkdX2UKGgGaAloD0MIuYybGmjOWECUhpRSlGgVTegDaBZHQITvAkTpPh11fZQoaAZoCWgPQwjzVIfcDJc5wJSGlFKUaBVNdgFoFkdAhPhQpe/pMnV9lChoBmgJaA9DCJTcYROZM2JAlIaUUpRoFU3oA2gWR0CE+Yz9jwx4dX2UKGgGaAloD0MI9bnaiv0nW0CUhpRSlGgVTegDaBZHQIUOwuVX3g11fZQoaAZoCWgPQwjdlV0wuGpVQJSGlFKUaBVN6ANoFkdAhRRBf8dgfHV9lChoBmgJaA9DCIeGxahr411AlIaUUpRoFU3oA2gWR0CFH3KDCgscdX2UKGgGaAloD0MIOPdXj/ugYkCUhpRSlGgVTegDaBZHQIU+mgJ1JUZ1fZQoaAZoCWgPQwgXSbvRx31WQJSGlFKUaBVN6ANoFkdAhUPH2ys0YXV9lChoBmgJaA9DCJlho6zf61dAlIaUUpRoFU3oA2gWR0CFT5Jbt7a7dX2UKGgGaAloD0MI5nRZTGw6XkCUhpRSlGgVTegDaBZHQIVlUijcmBx1fZQoaAZoCWgPQwhRpPs5BbVXQJSGlFKUaBVN6ANoFkdAhXBC9Iwud3V9lChoBmgJaA9DCBAHCVG+ZlZAlIaUUpRoFU3oA2gWR0CFdHyEtdzGdX2UKGgGaAloD0MId0gxQKLVXUCUhpRSlGgVTegDaBZHQIV3G6Zpi7V1fZQoaAZoCWgPQwgFGJY/365SQJSGlFKUaBVN6ANoFkdAhXpzXJ5miHV9lChoBmgJaA9DCPvrFRbc1FpAlIaUUpRoFU3oA2gWR0CFxbej2zv7dX2UKGgGaAloD0MIqg65Ge5yYECUhpRSlGgVTegDaBZHQIXIO6unuRd1fZQoaAZoCWgPQwhWgzC3++VgQJSGlFKUaBVN6ANoFkdAhdCMOwxFiXV9lChoBmgJaA9DCIhp39xfBSfAlIaUUpRoFUvaaBZHQIXVCQ9zOop1fZQoaAZoCWgPQwiPqFDdXO5ZQJSGlFKUaBVN6ANoFkdAhdnCHZbpvHV9lChoBmgJaA9DCGv0aoDSnVJAlIaUUpRoFU3oA2gWR0CF2wOktVaPdX2UKGgGaAloD0MIfCjRksfUU0CUhpRSlGgVTegDaBZHQIXuEYuTRpl1fZQoaAZoCWgPQwjMeca+ZPhbQJSGlFKUaBVN6ANoFkdAhfLf2Cdz4nV9lChoBmgJaA9DCBvUfmsnSvK/lIaUUpRoFUv6aBZHQIX6xng5zYF1fZQoaAZoCWgPQwgn+RG/YjlWQJSGlFKUaBVN6ANoFkdAhf0IJAt4A3V9lChoBmgJaA9DCPOTap+OCmFAlIaUUpRoFU3oA2gWR0CGGwETxoZidX2UKGgGaAloD0MIQEtXsA22YUCUhpRSlGgVTegDaBZHQIYgTxG2Cul1fZQoaAZoCWgPQwiN74tLVfBbQJSGlFKUaBVN6ANoFkdAhiwiwSrYG3V9lChoBmgJaA9DCEIFhxfEnmlAlIaUUpRoFU3HAWgWR0CGPuSeRPoFdX2UKGgGaAloD0MItvRoqqenYUCUhpRSlGgVTegDaBZHQIZBNwFTvRZ1fZQoaAZoCWgPQwi7D0BqE8ZaQJSGlFKUaBVN6ANoFkdAhkt3M6ij+XV9lChoBmgJaA9DCGiz6nM1K2BAlIaUUpRoFU3oA2gWR0CGTw9ECvHMdX2UKGgGaAloD0MIV3xD4TPjYECUhpRSlGgVTegDaBZHQIZRambb1yx1fZQoaAZoCWgPQwi3ek5634hUQJSGlFKUaBVN6ANoFkdAhp0ZV4oqkXV9lChoBmgJaA9DCKMBvAUS/VVAlIaUUpRoFU3oA2gWR0CGn4TFERapdX2UKGgGaAloD0MI3GeVmdJZXUCUhpRSlGgVTegDaBZHQIanxccENfB1fZQoaAZoCWgPQwipFDsahzpXQJSGlFKUaBVN6ANoFkdAhrFAP3BYWHV9lChoBmgJaA9DCN9uSQ7YUVhAlIaUUpRoFU3oA2gWR0CGspZPEbYLdX2UKGgGaAloD0MIlIYahaTbYECUhpRSlGgVTegDaBZHQIbIA7LdN351fZQoaAZoCWgPQwjrGcIxy05OwJSGlFKUaBVNQQFoFkdAhtT5CF9KEnV9lChoBmgJaA9DCJgXYB+dIl1AlIaUUpRoFU3oA2gWR0CG1iZccENfdX2UKGgGaAloD0MIuCHGa96zYkCUhpRSlGgVTegDaBZHQIbYkR3/xUh1fZQoaAZoCWgPQwiRYoBEE+JcQJSGlFKUaBVN6ANoFkdAhvbDwQUYbnV9lChoBmgJaA9DCJZdMLjmaVhAlIaUUpRoFU3oA2gWR0CG+9yOq//OdX2UKGgGaAloD0MIhsd+FktVMECUhpRSlGgVTUcBaBZHQIb+dJWeYlZ1fZQoaAZoCWgPQwjbv7LSpDBgQJSGlFKUaBVN6ANoFkdAhwaw6IWP93V9lChoBmgJaA9DCFZl3xXBNlBAlIaUUpRoFU3oA2gWR0CHGKJyhi9adX2UKGgGaAloD0MIsz9Qbtv2W0CUhpRSlGgVTegDaBZHQIca5a9sabZ1fZQoaAZoCWgPQwjvU1VoIJJUQJSGlFKUaBVN6ANoFkdAhySl85S3s3V9lChoBmgJaA9DCEX2QZYF6lxAlIaUUpRoFU3oA2gWR0CHKF+IdlundX2UKGgGaAloD0MI63B0le6uCMCUhpRSlGgVTRUBaBZHQIcpXhAGB4F1fZQoaAZoCWgPQwhR3Vz87VZjQJSGlFKUaBVN6ANoFkdAhyqOZCv5g3V9lChoBmgJaA9DCPYksDkHm11AlIaUUpRoFU3oA2gWR0CHQOrOJLuhdWUu"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 124,
+    "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:db117dd637d50d47eb0e5d59fb16ec7a1e5791b168ea1ba2efa9c97fc1ffc125
+size 84829

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9346185cad6aa494779888204123f9078cc28bb029e7ee061edadae721d3ad7d
+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 sha256:8ea45603c7fc5dc25e425e2cdcf73b9cf91d8c0322d0c61a2c5bfe56f326a009
+size 245350

results.json

@@ -0,0 +1 @@
+{"mean_reward": 67.11697865687115, "std_reward": 97.37892373224659, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-05T16:18:07.983015"}