rl class unit 1 submission

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 205.64 +/- 9.39
+      name: mean_reward
+      verified: false
+---
+
+# **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
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

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 0x7fbac7b11050>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbac7b110e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbac7b11170>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbac7b11200>", "_build": "<function ActorCriticPolicy._build at 0x7fbac7b11290>", "forward": "<function ActorCriticPolicy.forward at 0x7fbac7b11320>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbac7b113b0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fbac7b11440>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbac7b114d0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbac7b11560>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbac7b115f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fbac7e35150>"}, "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": 1667739621376990512, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVwQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsBSwFLE0MEiABTAJROhZQpjAFflIWUjEgvdXNyL2xvY2FsL2xpYi9weXRob24zLjcvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuAQwIAAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEgvdXNyL2xvY2FsL2xpYi9weXRob24zLjcvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUdU5OaACMEF9tYWtlX2VtcHR5X2NlbGyUk5QpUpSFlHSUUpSMHGNsb3VkcGlja2xlLmNsb3VkcGlja2xlX2Zhc3SUjBJfZnVuY3Rpb25fc2V0c3RhdGWUk5RoH32UfZQoaBZoDYwMX19xdWFsbmFtZV9flIwZY29uc3RhbnRfZm4uPGxvY2Fscz4uZnVuY5SMD19fYW5ub3RhdGlvbnNfX5R9lIwOX19rd2RlZmF1bHRzX1+UTowMX19kZWZhdWx0c19flE6MCl9fbW9kdWxlX1+UaBeMB19fZG9jX1+UTowLX19jbG9zdXJlX1+UaACMCl9tYWtlX2NlbGyUk5RHPzOpKjBVMmGFlFKUhZSMF19jbG91ZHBpY2tsZV9zdWJtb2R1bGVzlF2UjAtfX2dsb2JhbHNfX5R9lHWGlIZSMC4="}, "_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-5.10.133+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.7.15", "Stable-Baselines3": "1.6.2", "PyTorch": "1.12.1+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo_lunar.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:82e904d753d3492e04538ec12ac1861b64b7b43efd672e605f0767e2270cc294
+size 147146

ppo_lunar/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.2

ppo_lunar/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 0x7fbac7b11050>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbac7b110e0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbac7b11170>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbac7b11200>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fbac7b11290>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fbac7b11320>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbac7b113b0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fbac7b11440>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbac7b114d0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbac7b11560>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbac7b115f0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fbac7e35150>"
+    },
+    "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": 1667739621376990512,
+    "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:": "gAWVehAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIFLTJ4ZPsRkCUhpRSlIwBbJRN6AOMAXSUR0CF8++B6KLsdX2UKGgGaAloD0MI1/Z2S3KGXUCUhpRSlGgVTegDaBZHQIX1VlqagEl1fZQoaAZoCWgPQwhbecn/5J8XwJSGlFKUaBVL4mgWR0CGAjRUm2LHdX2UKGgGaAloD0MITfbP04B9YcCUhpRSlGgVS8poFkdAhhJge7tiQXV9lChoBmgJaA9DCNk+5C1XdVxAlIaUUpRoFU3oA2gWR0CGG2F/QSi/dX2UKGgGaAloD0MI2jnNAm17ZECUhpRSlGgVTegDaBZHQIZKUe+23KB1fZQoaAZoCWgPQwiloNtLGrVVQJSGlFKUaBVN6ANoFkdAhkt0bT+efHV9lChoBmgJaA9DCB1yM9wAt2BAlIaUUpRoFU3oA2gWR0CGUib70nPWdX2UKGgGaAloD0MI1hpK7UWlWUCUhpRSlGgVTegDaBZHQIZeBA6dUbV1fZQoaAZoCWgPQwgFUmLX9ihZQJSGlFKUaBVN6ANoFkdAhmIRGDtgKHV9lChoBmgJaA9DCGE3bFuUhFBAlIaUUpRoFU3oA2gWR0CGY9IkJKJ3dX2UKGgGaAloD0MIceMW83PhUkCUhpRSlGgVTegDaBZHQIZlKRfWtlt1fZQoaAZoCWgPQwi13QTfNFteQJSGlFKUaBVN6ANoFkdAhmsSKekHlnV9lChoBmgJaA9DCDxodt1bKltAlIaUUpRoFU3oA2gWR0CGetZ/0/W2dX2UKGgGaAloD0MIM9/BTxxnU0CUhpRSlGgVTegDaBZHQIZ9KZ8a4tp1fZQoaAZoCWgPQwhsPxnjw+FgQJSGlFKUaBVN6ANoFkdAhoiLY5DJEHV9lChoBmgJaA9DCMVx4NXyAmZAlIaUUpRoFU0TAmgWR0CGjTOzposadX2UKGgGaAloD0MIasL2kzEBVkCUhpRSlGgVTegDaBZHQIaWQood+5R1fZQoaAZoCWgPQwhSnKOOjssZwJSGlFKUaBVL3GgWR0CGqvSeiBXkdX2UKGgGaAloD0MItJPBUfLIS0CUhpRSlGgVTegDaBZHQIawCBTXJ5p1fZQoaAZoCWgPQwidZRah2KpTQJSGlFKUaBVN6ANoFkdAhrwwNTcZcnV9lChoBmgJaA9DCLEwRE5fp1JAlIaUUpRoFU3oA2gWR0CGyfIOH310dX2UKGgGaAloD0MIZtr+lZXCTsCUhpRSlGgVS+xoFkdAhs9M3AEdNnV9lChoBmgJaA9DCIdREDy+6WBAlIaUUpRoFU3oA2gWR0CG0r1kDp1SdX2UKGgGaAloD0MINnhflQvTZECUhpRSlGgVTegDaBZHQIbTtImPYFt1fZQoaAZoCWgPQwhIiPIFLapMQJSGlFKUaBVN6ANoFkdAhv8OBlMAWHV9lChoBmgJaA9DCIxNK4VAHVRAlIaUUpRoFU3oA2gWR0CHCP9BKL88dX2UKGgGaAloD0MIUADFyJLVWkCUhpRSlGgVTegDaBZHQIcMqkEcKgJ1fZQoaAZoCWgPQwjequtQTfxSQJSGlFKUaBVN6ANoFkdAhw5N/4Irv3V9lChoBmgJaA9DCFYo0v2cDV5AlIaUUpRoFU3oA2gWR0CHD4Z5zHS4dX2UKGgGaAloD0MIajNOQ1QBXUCUhpRSlGgVTegDaBZHQIcU0F+uvEF1fZQoaAZoCWgPQwgPK9zykUQnQJSGlFKUaBVNQgFoFkdAhxvURFqi5HV9lChoBmgJaA9DCMeEmEuq7lhAlIaUUpRoFU3oA2gWR0CHInfUF0PpdX2UKGgGaAloD0MIh6jCn+EHXECUhpRSlGgVTegDaBZHQIckrSiM5wR1fZQoaAZoCWgPQwi7K7tg8KRiQJSGlFKUaBVN6ANoFkdAhzK2St/4I3V9lChoBmgJaA9DCEPmyqDatDlAlIaUUpRoFU3oA2gWR0CHO8STQmeEdX2UKGgGaAloD0MI0O6QYoATV0CUhpRSlGgVTegDaBZHQIdKl+w1R+B1fZQoaAZoCWgPQwg6eCY0yXVhQJSGlFKUaBVN6ANoFkdAh1xeaz/p+3V9lChoBmgJaA9DCBnJHqFmlltAlIaUUpRoFU3oA2gWR0CHa2hpQDV6dX2UKGgGaAloD0MI12oPeyESY0CUhpRSlGgVTegDaBZHQIdxycAiml91fZQoaAZoCWgPQwi46c9+pNJbQJSGlFKUaBVN6ANoFkdAh3cZha1Ti3V9lChoBmgJaA9DCGPRdHYy8VlAlIaUUpRoFU3oA2gWR0CHpEp7TlT4dX2UKGgGaAloD0MIVYZxN4jAXkCUhpRSlGgVTegDaBZHQIeweT7l7t11fZQoaAZoCWgPQwjrNxPThTRiQJSGlFKUaBVN6ANoFkdAh7R1tfoicHV9lChoBmgJaA9DCOZ3msx4il9AlIaUUpRoFU3oA2gWR0CHtl1dxAB1dX2UKGgGaAloD0MIRPmCFhIgWUCUhpRSlGgVTegDaBZHQIe3p/smfGx1fZQoaAZoCWgPQwhPrimQ2bVCwJSGlFKUaBVL9GgWR0CHvMBqbjLkdX2UKGgGaAloD0MILekoB7NBJkCUhpRSlGgVTegDaBZHQIe9EPz4DcN1fZQoaAZoCWgPQwj4bYjxmmtfQJSGlFKUaBVN6ANoFkdAh8PigkC3gHV9lChoBmgJaA9DCJUMAFXcRGRAlIaUUpRoFU3oA2gWR0CHyjTuv2XcdX2UKGgGaAloD0MI5neazHjiUkCUhpRSlGgVTegDaBZHQIfMMHyEtd11fZQoaAZoCWgPQwgfZi/bTrsFwJSGlFKUaBVLwGgWR0CH0NOPeYUndX2UKGgGaAloD0MImUuqtpuqXUCUhpRSlGgVTegDaBZHQIfY+z6ab4J1fZQoaAZoCWgPQwg4Mo/8weFWQJSGlFKUaBVN6ANoFkdAh+FK9GqgiHV9lChoBmgJaA9DCH7ja88sE2BAlIaUUpRoFU3oA2gWR0CH78CfYjB3dX2UKGgGaAloD0MIOQzmr5AnYUCUhpRSlGgVTegDaBZHQIgAnq7iADt1fZQoaAZoCWgPQwhwCcA/JXNgQJSGlFKUaBVN6ANoFkdAiA8C3G4qgHV9lChoBmgJaA9DCKhwBKkUo0tAlIaUUpRoFUv1aBZHQIgRByU9pyp1fZQoaAZoCWgPQwgijJ/GvXhRQJSGlFKUaBVN6ANoFkdAiBSBSDRMOHV9lChoBmgJaA9DCBwlr84xBVpAlIaUUpRoFU3oA2gWR0CIH3WQwK0EdX2UKGgGaAloD0MIMBNFSF1xYUCUhpRSlGgVTegDaBZHQIhRCx3V0911fZQoaAZoCWgPQwhuTbotkbRRQJSGlFKUaBVN6ANoFkdAiFUYoiLVF3V9lChoBmgJaA9DCGjsSzYeDkdAlIaUUpRoFU3oA2gWR0CIVuk5ZKWcdX2UKGgGaAloD0MIba6a5wjgYUCUhpRSlGgVTegDaBZHQIhYNQbdadN1fZQoaAZoCWgPQwgZx0j2CLdZQJSGlFKUaBVN6ANoFkdAiF4oOx0MgHV9lChoBmgJaA9DCHlA2ZQrp2NAlIaUUpRoFU3oA2gWR0CIZfqrzXjEdX2UKGgGaAloD0MIlX1XBP8WYkCUhpRSlGgVTegDaBZHQIhtJaaCtih1fZQoaAZoCWgPQwhuMNRhhXJbQJSGlFKUaBVN6ANoFkdAiG+ZEDyOJnV9lChoBmgJaA9DCFILJZPT0GBAlIaUUpRoFU3oA2gWR0CIdOXrt3OfdX2UKGgGaAloD0MIord4eE9UYkCUhpRSlGgVTegDaBZHQIh+BiNKh+R1fZQoaAZoCWgPQwhGeeblsIheQJSGlFKUaBVN6ANoFkdAiIfWTX8O1HV9lChoBmgJaA9DCIyeW+hKlDpAlIaUUpRoFU0GAWgWR0CIj0RVZLZjdX2UKGgGaAloD0MIUMO3sG4dV0CUhpRSlGgVTegDaBZHQIiqglUp/gB1fZQoaAZoCWgPQwhCBvLs8nhgQJSGlFKUaBVN6ANoFkdAiLtKIrOJL3V9lChoBmgJaA9DCJwwYTQrxlpAlIaUUpRoFU3oA2gWR0CIvZ6uW8h+dX2UKGgGaAloD0MI+G9enPg6IcCUhpRSlGgVTegDaBZHQIjCAG2TgVJ1fZQoaAZoCWgPQwgi/fZ14LRaQJSGlFKUaBVN6ANoFkdAiM9DLB9Cu3V9lChoBmgJaA9DCIMVp1oLY2FAlIaUUpRoFU3oA2gWR0CJA781n/T9dX2UKGgGaAloD0MI7mDEPgGoWkCUhpRSlGgVTegDaBZHQIkIY1ivxH51fZQoaAZoCWgPQwhLyXISynhgQJSGlFKUaBVN6ANoFkdAiQpXE61b7nV9lChoBmgJaA9DCIY41sVtKF9AlIaUUpRoFU3oA2gWR0CJC9q0MPSVdX2UKGgGaAloD0MIJQSr6uWMVUCUhpRSlGgVTegDaBZHQIkSmq7yxzJ1fZQoaAZoCWgPQwh+ObNdIaJhQJSGlFKUaBVN6ANoFkdAiRtCJ40Mw3V9lChoBmgJaA9DCHI2HQFcfGNAlIaUUpRoFU3oA2gWR0CJJjAUtZmqdX2UKGgGaAloD0MIrYpwk1EdTECUhpRSlGgVTQIBaBZHQIkm8x46fap1fZQoaAZoCWgPQwgk8fJ0rtw2QJSGlFKUaBVN6ANoFkdAiSzfvWpZOnV9lChoBmgJaA9DCOBL4UGz6xxAlIaUUpRoFU0LAWgWR0CJLRy3kPtldX2UKGgGaAloD0MIn1p9dVXZVUCUhpRSlGgVTegDaBZHQIk1THlwLmZ1fZQoaAZoCWgPQwjr5AzFHfFWQJSGlFKUaBVN6ANoFkdAiT2sHSnccnV9lChoBmgJaA9DCLqj/+VaAV1AlIaUUpRoFU3oA2gWR0CJRDWiDdxidX2UKGgGaAloD0MIcCcR4V90EkCUhpRSlGgVTRcBaBZHQIlOrB42S+x1fZQoaAZoCWgPQwhssHCS5v8pQJSGlFKUaBVNGAFoFkdAiU8RW912aHV9lChoBmgJaA9DCOZatABtCynAlIaUUpRoFU0gAWgWR0CJWZzHS4OMdX2UKGgGaAloD0MInKIjufx+W0CUhpRSlGgVTegDaBZHQIlcNbkfcN91fZQoaAZoCWgPQwg2zqYjgMpdQJSGlFKUaBVN6ANoFkdAiWqKwY+B6XV9lChoBmgJaA9DCIOkT6vobxzAlIaUUpRoFU0rAWgWR0CJbDDrqt5ldX2UKGgGaAloD0MIMEeP39udY0CUhpRSlGgVTegDaBZHQIlsbYGt6ol1fZQoaAZoCWgPQwidR8X/HW5jQJSGlFKUaBVN6ANoFkdAiW/3NLUTc3V9lChoBmgJaA9DCAzNdRppPGJAlIaUUpRoFU3oA2gWR0CJewvNeMQ3dX2UKGgGaAloD0MIT8+7saCwKsCUhpRSlGgVTRgBaBZHQImBBWcSXdF1ZS4="
+    },
+    "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_lunar/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:224e858f04d6463bd5ed34cff285c58e2cf462bdc08456f4193a570da7f6b1a1
+size 87865

ppo_lunar/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:075c6367839e968453ccb29c89366938de47fb89bb1266a2b3abc00bdbaccc38
+size 43201

ppo_lunar/pytorch_variables.pth

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

ppo_lunar/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Fri Aug 26 08:44:51 UTC 2022
+Python: 3.7.15
+Stable-Baselines3: 1.6.2
+PyTorch: 1.12.1+cu113
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 205.6370254421467, "std_reward": 9.385561268687157, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-11-06T13:16:42.652791"}