first commit of Luna_lender

.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

Luna_lender_kharitonov.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:58f491d5c362da6435b7d4a722d8ac5cc237bcb14c0b7ba8f61810a7ee0c459d
+size 144016

Luna_lender_kharitonov/_stable_baselines3_version

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

Luna_lender_kharitonov/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 0x7faae40034d0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7faae4003560>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7faae40035f0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7faae4003680>",
+        "_build": "<function ActorCriticPolicy._build at 0x7faae4003710>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7faae40037a0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7faae4003830>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7faae40038c0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7faae4003950>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7faae40039e0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7faae4003a70>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7faae3fca9f0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAAAAAAAAAACUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAAAAAAAAAAAlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu",
+        "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": 1651824017.6213288,
+    "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
+}

Luna_lender_kharitonov/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3fb709bb4deb59098e4e67c40693aa6ddb76a9f2d4de9dbee25c345e9bc0a72c
+size 84829

Luna_lender_kharitonov/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fa7ae6d36978d3def0e64b2726aec6c8281c858b260a71ec010360fda2fecfeb
+size 43201

Luna_lender_kharitonov/pytorch_variables.pth

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

Luna_lender_kharitonov/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

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: 235.49 +/- 22.57
+      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 0x7faae40034d0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7faae4003560>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7faae40035f0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7faae4003680>", "_build": "<function ActorCriticPolicy._build at 0x7faae4003710>", "forward": "<function ActorCriticPolicy.forward at 0x7faae40037a0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7faae4003830>", "_predict": "<function ActorCriticPolicy._predict at 0x7faae40038c0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7faae4003950>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7faae40039e0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7faae4003a70>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7faae3fca9f0>"}, "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": 1651824017.6213288, "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:": "gAWVvwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwNX2J1aWx0aW5fdHlwZZSTlIwKTGFtYmRhVHlwZZSFlFKUKGgCjAhDb2RlVHlwZZSFlFKUKEsBSwBLAUsBSxNDBIgAUwCUToWUKYwBX5SFlIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5RLgEMCAAGUjAN2YWyUhZQpdJRSlH2UKIwLX19wYWNrYWdlX1+UjBhzdGFibGVfYmFzZWxpbmVzMy5jb21tb26UjAhfX25hbWVfX5SMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMCF9fZmlsZV9flIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaCB9lH2UKGgXaA6MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgYjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "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"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1bcf7103fd8e93eb1e4e7bc15ed472b68accda6c011e004f8e2426985b9a9297
+size 248908

results.json

@@ -0,0 +1 @@
+{"mean_reward": 235.48564825664735, "std_reward": 22.568907513806945, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-06T08:39:05.822875"}