init

.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: 175.26 +/- 80.01
+      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 0x7f1b96d77d40>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1b96d77dd0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1b96d77e60>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1b96d77ef0>", "_build": "<function ActorCriticPolicy._build at 0x7f1b96d77f80>", "forward": "<function ActorCriticPolicy.forward at 0x7f1b96d7f050>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1b96d7f0e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f1b96d7f170>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1b96d7f200>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1b96d7f290>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1b96d7f320>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f1b96dc8780>"}, "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": 1652964987.0113246, "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:": "gAWVchAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMI/BwfLc7uYUCUhpRSlIwBbJRN6AOMAXSUR0CEYr/mT1TSdX2UKGgGaAloD0MIZhah2AoeP8CUhpRSlGgVS9doFkdAhHfyCFsYVXV9lChoBmgJaA9DCDLjbaVXr2BAlIaUUpRoFU3oA2gWR0CEeIgX/HYIdX2UKGgGaAloD0MI1ZY6yOthRkCUhpRSlGgVTegDaBZHQITT6e05U991fZQoaAZoCWgPQwgVGohlM5FfQJSGlFKUaBVN6ANoFkdAhNVJx3mmtXV9lChoBmgJaA9DCPjj9ssnmzfAlIaUUpRoFUvQaBZHQITd77TDwYt1fZQoaAZoCWgPQwh1kNeDSV0/QJSGlFKUaBVNIAFoFkdAhOsnggow23V9lChoBmgJaA9DCIJXy52ZhFpAlIaUUpRoFU3oA2gWR0CE9r8Sf16FdX2UKGgGaAloD0MISZ2AJsKmM8CUhpRSlGgVS9BoFkdAhPfSIpH7QHV9lChoBmgJaA9DCHNmu0IfzFZAlIaUUpRoFU3oA2gWR0CE+E+rU9ZBdX2UKGgGaAloD0MItTUiGAdrU0CUhpRSlGgVTegDaBZHQIULPR9gF5h1fZQoaAZoCWgPQwgDYDyDBnBgQJSGlFKUaBVN6ANoFkdAhRIlLnLaEnV9lChoBmgJaA9DCDYC8br+d2FAlIaUUpRoFU3oA2gWR0CFEtooNNJwdX2UKGgGaAloD0MI1nCRe7q4XkCUhpRSlGgVTegDaBZHQIUh8gGKQ7t1fZQoaAZoCWgPQwiwrgrUYvpTQJSGlFKUaBVN6ANoFkdAhSTyuIRAbHV9lChoBmgJaA9DCDCd1m1QIyjAlIaUUpRoFUvEaBZHQIUqhkmQbMp1fZQoaAZoCWgPQwjGwDqOH1hDQJSGlFKUaBVN6ANoFkdAhS8c9Oh0yXV9lChoBmgJaA9DCOgWuhKB52FAlIaUUpRoFU3oA2gWR0CFQl6JqIrOdX2UKGgGaAloD0MIS1rxDQVqY0CUhpRSlGgVTegDaBZHQIVJWGIsRQJ1fZQoaAZoCWgPQwjB4QURqSRYQJSGlFKUaBVN6ANoFkdAhUosMqjJuHV9lChoBmgJaA9DCHO6LCY2AVtAlIaUUpRoFU3oA2gWR0CFSwNNrTH9dX2UKGgGaAloD0MISBlxAWh0IUCUhpRSlGgVTTEBaBZHQIV0NXV9Wp91fZQoaAZoCWgPQwjGF+3xQtpZQJSGlFKUaBVN6ANoFkdAhbk3cQAdXHV9lChoBmgJaA9DCEc9RKM7DFxAlIaUUpRoFU3oA2gWR0CFwmmBOHnEdX2UKGgGaAloD0MIFVW/0vmQV0CUhpRSlGgVTegDaBZHQIXPn779AHF1fZQoaAZoCWgPQwjkvP+PE6byP5SGlFKUaBVLvGgWR0CF2ZChvitJdX2UKGgGaAloD0MIzo3pCUsGXUCUhpRSlGgVTegDaBZHQIXam/vfCQ91fZQoaAZoCWgPQwh4eqUsQ0lcQJSGlFKUaBVN6ANoFkdAhdutCqp97XV9lChoBmgJaA9DCBLYnIPnSmBAlIaUUpRoFU3oA2gWR0CF3CA+Y+jedX2UKGgGaAloD0MIdmuZDMdgYECUhpRSlGgVTegDaBZHQIXzAHE/B311fZQoaAZoCWgPQwjNHmgFBqNgQJSGlFKUaBVN6ANoFkdAhfOnIQvpQnV9lChoBmgJaA9DCEOPGD23ECJAlIaUUpRoFUvraBZHQIX9H0h/y5J1fZQoaAZoCWgPQwgicCTQ4J5jQJSGlFKUaBVN6ANoFkdAhgJxXXAdn3V9lChoBmgJaA9DCJI81/fhg2BAlIaUUpRoFU3oA2gWR0CGBWiUxEfDdX2UKGgGaAloD0MICRfyCG4OYkCUhpRSlGgVTegDaBZHQIYKqClJpWV1fZQoaAZoCWgPQwhpjxfSYbxgQJSGlFKUaBVN6ANoFkdAhg6/WlMyrXV9lChoBmgJaA9DCH9Ma9PY/i1AlIaUUpRoFUuFaBZHQIYiDvZyuIR1fZQoaAZoCWgPQwjXhLTGoDhbQJSGlFKUaBVN6ANoFkdAhiaQtapxWHV9lChoBmgJaA9DCKTFGcOcy1dAlIaUUpRoFU3oA2gWR0CGJ00gr6LwdX2UKGgGaAloD0MI3o0FhUGlTkCUhpRSlGgVTegDaBZHQIYoGqioKlZ1fZQoaAZoCWgPQwge39416Ns7QJSGlFKUaBVL4mgWR0CGK28jAzpHdX2UKGgGaAloD0MI+boM/2kNYUCUhpRSlGgVTegDaBZHQIZOY6dUbUB1fZQoaAZoCWgPQwjmApfHmrNHQJSGlFKUaBVNFgFoFkdAhlLlQ/HHWHV9lChoBmgJaA9DCFO0ci8wLFlAlIaUUpRoFU3oA2gWR0CGnp6zmfXgdX2UKGgGaAloD0MIAYi7ehUpX0CUhpRSlGgVTegDaBZHQIas1iSaEzx1fZQoaAZoCWgPQwhAbOnRVBNjQJSGlFKUaBVN6ANoFkdAhrjkkB0ZFXV9lChoBmgJaA9DCJmaBG9IBlZAlIaUUpRoFU3oA2gWR0CGug31jAi3dX2UKGgGaAloD0MIMq8jDll4YECUhpRSlGgVTegDaBZHQIa6m3pfQa91fZQoaAZoCWgPQwjiWBe30YhjQJSGlFKUaBVN6ANoFkdAhtRY51eSjnV9lChoBmgJaA9DCI4Dr5Y7fmFAlIaUUpRoFU3oA2gWR0CG1Qy9EkSmdX2UKGgGaAloD0MIN1SM8zfdM0CUhpRSlGgVS71oFkdAhtnkfcN6PnV9lChoBmgJaA9DCC4B+KdU42NAlIaUUpRoFU3oA2gWR0CG36o86mwadX2UKGgGaAloD0MItqD3xhARV0CUhpRSlGgVTegDaBZHQIblX4Kx9oh1fZQoaAZoCWgPQwh2G9R+a71fQJSGlFKUaBVN6ANoFkdAhugpo9LYgHV9lChoBmgJaA9DCLfte9Rf7F1AlIaUUpRoFU3oA2gWR0CHCcR7qptKdX2UKGgGaAloD0MIeXk6V5QlXUCUhpRSlGgVTegDaBZHQIcPPPeHi3p1fZQoaAZoCWgPQwjf+xu0V1piQJSGlFKUaBVN6ANoFkdAhxAl0xM363V9lChoBmgJaA9DCJolAWpqZTRAlIaUUpRoFUvraBZHQIcQ2M6zVtp1fZQoaAZoCWgPQwijQJ/IkxReQJSGlFKUaBVN6ANoFkdAhxVsBhhH9XV9lChoBmgJaA9DCLO1vkhokGFAlIaUUpRoFU3oA2gWR0CHP1xLCemOdX2UKGgGaAloD0MIbVhTWRSeXECUhpRSlGgVTegDaBZHQIdEcY8+zMR1fZQoaAZoCWgPQwgg09o0NthlQJSGlFKUaBVN6ANoFkdAh5J92Pkq+nV9lChoBmgJaA9DCDntKTknjVxAlIaUUpRoFU3oA2gWR0CHoWdZq20BdX2UKGgGaAloD0MIm8qisIurXUCUhpRSlGgVTegDaBZHQIetnuTibUh1fZQoaAZoCWgPQwjcoPZbO6dWQJSGlFKUaBVN6ANoFkdAh69GoBJZn3V9lChoBmgJaA9DCJ/Ik6RrHEJAlIaUUpRoFUv6aBZHQIe1ydz4k/t1fZQoaAZoCWgPQwi22y4019pZQJSGlFKUaBVN6ANoFkdAh8nXUQTVUnV9lChoBmgJaA9DCNdrelBQ3lpAlIaUUpRoFU3oA2gWR0CHyoFGoaUBdX2UKGgGaAloD0MIW5pbIazWV0CUhpRSlGgVTegDaBZHQIfPXMlkYoB1fZQoaAZoCWgPQwh381SH3PpeQJSGlFKUaBVN6ANoFkdAh9TxUWEbpHV9lChoBmgJaA9DCMaLhSFyqmJAlIaUUpRoFU3oA2gWR0CH2mXyAhB7dX2UKGgGaAloD0MIcm2oGOfXP0CUhpRSlGgVS9NoFkdAh+xgwfyPMnV9lChoBmgJaA9DCAO0rWadtFpAlIaUUpRoFU3oA2gWR0CH/jHvMKTjdX2UKGgGaAloD0MI5j3ONGHnXUCUhpRSlGgVTegDaBZHQIgDaLn9vTB1fZQoaAZoCWgPQwjy0He3sgFcQJSGlFKUaBVN6ANoFkdAiARA0CRwInV9lChoBmgJaA9DCJOpglFJsWBAlIaUUpRoFU3oA2gWR0CIBOqo60Y1dX2UKGgGaAloD0MIUOPe/IYHW0CUhpRSlGgVTegDaBZHQIgIudRR/Ex1fZQoaAZoCWgPQwhU5BBxc2oIwJSGlFKUaBVNMQFoFkdAiAv17hNucnV9lChoBmgJaA9DCK7YX3ZPmlxAlIaUUpRoFU3oA2gWR0CIK1KyOaOQdX2UKGgGaAloD0MI8rbSa7PPV0CUhpRSlGgVTegDaBZHQIh6k2P1ct51fZQoaAZoCWgPQwjPoQxVMS9tQJSGlFKUaBVNdgFoFkdAiIAA9FF2FHV9lChoBmgJaA9DCCy8y0V8GV9AlIaUUpRoFU3oA2gWR0CIiJVYISlFdX2UKGgGaAloD0MIshLzrKThT0CUhpRSlGgVTegDaBZHQIiT0Tzundh1fZQoaAZoCWgPQwjzOXe73hdiQJSGlFKUaBVN6ANoFkdAiJVk6Lfk3nV9lChoBmgJaA9DCH3rw3ojl2FAlIaUUpRoFU3oA2gWR0CImyL9deIEdX2UKGgGaAloD0MIZF3cRgNkMECUhpRSlGgVS+poFkdAiKIqZ2IO6XV9lChoBmgJaA9DCIUGYtnMemFAlIaUUpRoFU3oA2gWR0CIrVNyo4uLdX2UKGgGaAloD0MIBeEKKNQ8YECUhpRSlGgVTegDaBZHQIiymiFj/dZ1fZQoaAZoCWgPQwhG7X4V4J9bQJSGlFKUaBVN6ANoFkdAiLfyQo1DSnV9lChoBmgJaA9DCPNxbagYezDAlIaUUpRoFUvnaBZHQIi4P3pOerd1fZQoaAZoCWgPQwihMZOoFzRIQJSGlFKUaBVL5GgWR0CIuSAWi1zAdX2UKGgGaAloD0MI3UWYolzbWkCUhpRSlGgVTegDaBZHQIjNplYlpoN1fZQoaAZoCWgPQwjjiSDOw/U2wJSGlFKUaBVNBgFoFkdAiNTrLIPsiXV9lChoBmgJaA9DCL+36c9+GWBAlIaUUpRoFU3oA2gWR0CI3emelKsddX2UKGgGaAloD0MILiC0Hr5JXECUhpRSlGgVTegDaBZHQIjilspG4I91fZQoaAZoCWgPQwj4pX7e1N9gQJSGlFKUaBVN6ANoFkdAiOQM+V1OkHV9lChoBmgJaA9DCJpeYizTrVlAlIaUUpRoFU3oA2gWR0CI6CtFrl/6dX2UKGgGaAloD0MIY0FhUKarXkCUhpRSlGgVTegDaBZHQIjroLRa5gB1fZQoaAZoCWgPQwhN27+y0s9gQJSGlFKUaBVN6ANoFkdAiQzagdwNsnV9lChoBmgJaA9DCIYA4Nizv1NAlIaUUpRoFU0AAWgWR0CJEheqJdjYdWUu"}, "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"}}

nomansland.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:073392da272667a39706da97a40f94e6f415a9533475e4e5d91300307109f8cb
+size 144028

nomansland/_stable_baselines3_version

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

nomansland/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 0x7f1b96d77d40>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1b96d77dd0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1b96d77e60>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1b96d77ef0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f1b96d77f80>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f1b96d7f050>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1b96d7f0e0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f1b96d7f170>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1b96d7f200>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1b96d7f290>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1b96d7f320>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f1b96dc8780>"
+    },
+    "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": 1652964987.0113246,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWVvwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwNX2J1aWx0aW5fdHlwZZSTlIwKTGFtYmRhVHlwZZSFlFKUKGgCjAhDb2RlVHlwZZSFlFKUKEsBSwBLAUsBSxNDBIgAUwCUToWUKYwBX5SFlIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5RLgEMCAAGUjAN2YWyUhZQpdJRSlH2UKIwLX19wYWNrYWdlX1+UjBhzdGFibGVfYmFzZWxpbmVzMy5jb21tb26UjAhfX25hbWVfX5SMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMCF9fZmlsZV9flIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaCB9lH2UKGgXaA6MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgYjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz8zqSowVTJhhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"
+    },
+    "_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
+}

nomansland/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5b5fdae4ef82ea1010806a8f2b1f8db48fd96f9ac0bb84e1149c6f7bfde04e7d
+size 84829

nomansland/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:abe11f98b6e667bf7101bbb657480744a7624b4969658892c471bfdf4f1e3984
+size 43201

nomansland/pytorch_variables.pth

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

nomansland/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 sha256:4fe5ef84b7e96c329fee9d5394d8e677ce6cd5470dc99e03f1b4853b7702793c
+size 230103

results.json

@@ -0,0 +1 @@
+{"mean_reward": 175.26293010911732, "std_reward": 80.00968545075348, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-19T13:23:07.902496"}